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[wpimath] Use Odometry for internal state in Pose Estimation (#4668)

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This effectively replaces the Unscented Kalman Filter used for Pose Estimation with the Odometry model, and uses a recalculable Kalman gain matrix to update pose estimations whenever a vision measurement is added.

Notably, this change removes the need for the confusing generics used in Java, and the C++ implementation got quite a bit less complex as well.

Co-authored-by: Tyler Veness <calcmogul@gmail.com>
This commit is contained in:
Jordan McMichael
2022-12-02 11:36:10 -05:00
committed by GitHub
parent 68dba92630
commit e22d8cc343
35 changed files with 2288 additions and 1884 deletions
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4
styleguide/spotbugs-exclude.xml
View File

@@ -17,6 +17,10 @@
<Match>
<Bug pattern="DMI_RANDOM_USED_ONLY_ONCE" />
</Match>
<Match>
<Bug pattern="EC_BAD_ARRAY_COMPARE" />
<Class name="edu.wpi.first.math.estimator.SwerveDrivePoseEstimator$InterpolationRecord" />
</Match>
<Match>
<Bug pattern="EI_EXPOSE_REP" />
</Match>

2
wpilibcExamples/src/main/cpp/examples/DifferentialDrivePoseEstimator/cpp/Drivetrain.cpp
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@@ -27,8 +27,6 @@ void Drivetrain::Drive(units::meters_per_second_t xSpeed,
void Drivetrain::UpdateOdometry() {
m_poseEstimator.Update(m_gyro.GetRotation2d(),
{units::meters_per_second_t{m_leftEncoder.GetRate()},
units::meters_per_second_t{m_rightEncoder.GetRate()}},
units::meter_t{m_leftEncoder.GetDistance()},
units::meter_t{m_rightEncoder.GetDistance()});

4
wpilibcExamples/src/main/cpp/examples/DifferentialDrivePoseEstimator/include/Drivetrain.h
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@@ -79,12 +79,12 @@ class Drivetrain {
// Gains are for example purposes only - must be determined for your own
// robot!
frc::DifferentialDrivePoseEstimator m_poseEstimator{
m_kinematics,
m_gyro.GetRotation2d(),
units::meter_t{m_leftEncoder.GetDistance()},
units::meter_t{m_rightEncoder.GetDistance()},
frc::Pose2d{},
{0.01, 0.01, 0.01, 0.01, 0.01},
{0.1, 0.1, 0.1},
{0.01, 0.01, 0.01},
{0.1, 0.1, 0.1}};
// Gains are for example purposes only - must be determined for your own

3
wpilibcExamples/src/main/cpp/examples/MecanumDrivePoseEstimator/cpp/Drivetrain.cpp
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@@ -56,8 +56,7 @@ void Drivetrain::Drive(units::meters_per_second_t xSpeed,
}
void Drivetrain::UpdateOdometry() {
m_poseEstimator.Update(m_gyro.GetRotation2d(), GetCurrentState(),
GetCurrentDistances());
m_poseEstimator.Update(m_gyro.GetRotation2d(), GetCurrentDistances());
// Also apply vision measurements. We use 0.3 seconds in the past as an
// example -- on a real robot, this must be calculated based either on latency

9
wpilibcExamples/src/main/cpp/examples/MecanumDrivePoseEstimator/include/Drivetrain.h
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@@ -77,11 +77,6 @@ class Drivetrain {
// Gains are for example purposes only - must be determined for your own
// robot!
frc::MecanumDrivePoseEstimator m_poseEstimator{
m_gyro.GetRotation2d(),
GetCurrentDistances(),
frc::Pose2d{},
m_kinematics,
{0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1},
{0.05, 0.05, 0.05, 0.05, 0.05},
{0.1, 0.1, 0.1}};
m_kinematics, m_gyro.GetRotation2d(), GetCurrentDistances(),
frc::Pose2d{}, {0.1, 0.1, 0.1}, {0.1, 0.1, 0.1}};
};

2
wpilibcExamples/src/main/cpp/examples/SwerveDrivePoseEstimator/cpp/Drivetrain.cpp
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@@ -28,8 +28,6 @@ void Drivetrain::Drive(units::meters_per_second_t xSpeed,
void Drivetrain::UpdateOdometry() {
m_poseEstimator.Update(m_gyro.GetRotation2d(),
{m_frontLeft.GetState(), m_frontRight.GetState(),
m_backLeft.GetState(), m_backRight.GetState()},
{m_frontLeft.GetPosition(), m_frontRight.GetPosition(),
m_backLeft.GetPosition(), m_backRight.GetPosition()});

5
wpilibcExamples/src/main/cpp/examples/SwerveDrivePoseEstimator/include/Drivetrain.h
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@@ -50,12 +50,11 @@ class Drivetrain {
// Gains are for example purposes only - must be determined for your own
// robot!
frc::SwerveDrivePoseEstimator<4> m_poseEstimator{
m_kinematics,
frc::Rotation2d{},
{m_frontLeft.GetPosition(), m_frontRight.GetPosition(),
m_backLeft.GetPosition(), m_backRight.GetPosition()},
frc::Pose2d{},
m_kinematics,
{0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1},
{0.05, 0.05, 0.05, 0.05, 0.05},
{0.1, 0.1, 0.1},
{0.1, 0.1, 0.1}};
};

9
wpilibjExamples/src/main/java/edu/wpi/first/wpilibj/examples/differentialdriveposeestimator/Drivetrain.java
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@@ -54,12 +54,12 @@ public class Drivetrain {
numbers used below are robot specific, and should be tuned. */
private final DifferentialDrivePoseEstimator m_poseEstimator =
new DifferentialDrivePoseEstimator(
m_kinematics,
m_gyro.getRotation2d(),
m_leftEncoder.getDistance(),
m_rightEncoder.getDistance(),
new Pose2d(),
VecBuilder.fill(0.05, 0.05, Units.degreesToRadians(5), 0.01, 0.01),
VecBuilder.fill(0.02, 0.02, Units.degreesToRadians(1)),
VecBuilder.fill(0.05, 0.05, Units.degreesToRadians(5)),
VecBuilder.fill(0.5, 0.5, Units.degreesToRadians(30)));
// Gains are for example purposes only - must be determined for your own robot!
@@ -118,10 +118,7 @@ public class Drivetrain {
/** Updates the field-relative position. */
public void updateOdometry() {
m_poseEstimator.update(
m_gyro.getRotation2d(),
new DifferentialDriveWheelSpeeds(m_leftEncoder.getRate(), m_rightEncoder.getRate()),
m_leftEncoder.getDistance(),
m_rightEncoder.getDistance());
m_gyro.getRotation2d(), m_leftEncoder.getDistance(), m_rightEncoder.getDistance());
// Also apply vision measurements. We use 0.3 seconds in the past as an example -- on
// a real robot, this must be calculated based either on latency or timestamps.

7
wpilibjExamples/src/main/java/edu/wpi/first/wpilibj/examples/mecanumdriveposeestimator/Drivetrain.java
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@@ -56,12 +56,11 @@ public class Drivetrain {
below are robot specific, and should be tuned. */
private final MecanumDrivePoseEstimator m_poseEstimator =
new MecanumDrivePoseEstimator(
m_kinematics,
m_gyro.getRotation2d(),
getCurrentDistances(),
new Pose2d(),
m_kinematics,
VecBuilder.fill(0.05, 0.05, Units.degreesToRadians(5), 0.05, 0.05, 0.05, 0.05),
VecBuilder.fill(Units.degreesToRadians(0.01), 0.01, 0.01, 0.01, 0.01),
VecBuilder.fill(0.05, 0.05, Units.degreesToRadians(5)),
VecBuilder.fill(0.5, 0.5, Units.degreesToRadians(30)));
// Gains are for example purposes only - must be determined for your own robot!
@@ -153,7 +152,7 @@ public class Drivetrain {
/** Updates the field relative position of the robot. */
public void updateOdometry() {
m_poseEstimator.update(m_gyro.getRotation2d(), getCurrentState(), getCurrentDistances());
m_poseEstimator.update(m_gyro.getRotation2d(), getCurrentDistances());
// Also apply vision measurements. We use 0.3 seconds in the past as an example -- on
// a real robot, this must be calculated based either on latency or timestamps.

22
wpilibjExamples/src/main/java/edu/wpi/first/wpilibj/examples/swervedriveposeestimator/Drivetrain.java
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@@ -4,7 +4,6 @@
package edu.wpi.first.wpilibj.examples.swervedriveposeestimator;
import edu.wpi.first.math.Nat;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.estimator.SwerveDrivePoseEstimator;
import edu.wpi.first.math.geometry.Pose2d;
@@ -12,9 +11,6 @@ import edu.wpi.first.math.geometry.Translation2d;
import edu.wpi.first.math.kinematics.ChassisSpeeds;
import edu.wpi.first.math.kinematics.SwerveDriveKinematics;
import edu.wpi.first.math.kinematics.SwerveModulePosition;
import edu.wpi.first.math.kinematics.SwerveModuleState;
import edu.wpi.first.math.numbers.N5;
import edu.wpi.first.math.numbers.N7;
import edu.wpi.first.math.util.Units;
import edu.wpi.first.wpilibj.AnalogGyro;
import edu.wpi.first.wpilibj.Timer;
@@ -42,11 +38,9 @@ public class Drivetrain {
/* Here we use SwerveDrivePoseEstimator so that we can fuse odometry readings. The numbers used
below are robot specific, and should be tuned. */
private final SwerveDrivePoseEstimator<N7, N7, N5> m_poseEstimator =
new SwerveDrivePoseEstimator<N7, N7, N5>(
Nat.N7(),
Nat.N7(),
Nat.N5(),
private final SwerveDrivePoseEstimator m_poseEstimator =
new SwerveDrivePoseEstimator(
m_kinematics,
m_gyro.getRotation2d(),
new SwerveModulePosition[] {
m_frontLeft.getPosition(),
@@ -55,9 +49,7 @@ public class Drivetrain {
m_backRight.getPosition()
},
new Pose2d(),
m_kinematics,
VecBuilder.fill(0.05, 0.05, Units.degreesToRadians(5), 0.05, 0.05, 0.05, 0.05),
VecBuilder.fill(Units.degreesToRadians(0.01), 0.01, 0.01, 0.01, 0.01),
VecBuilder.fill(0.05, 0.05, Units.degreesToRadians(5)),
VecBuilder.fill(0.5, 0.5, Units.degreesToRadians(30)));
public Drivetrain() {
@@ -89,12 +81,6 @@ public class Drivetrain {
public void updateOdometry() {
m_poseEstimator.update(
m_gyro.getRotation2d(),
new SwerveModuleState[] {
m_frontLeft.getState(),
m_frontRight.getState(),
m_backLeft.getState(),
m_backRight.getState()
},
new SwerveModulePosition[] {
m_frontLeft.getPosition(),
m_frontRight.getPosition(),

88
wpimath/algorithms.md Normal file
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@@ -0,0 +1,88 @@
# Algorithms
## Closed form Kalman gain for continuous Kalman filter with A = 0 and C = I
### Derivation
Model is
```
dx/dt = Ax + Bu
y = Cx + Du
```
where A = 0, B = 0, C = I, and D = 0.
The optimal cost-to-go is the P that satisfies
```
AᵀP + PA − PBR⁻¹BᵀP + Q = 0
```
Let A = Aᵀ and B = Cᵀ for state observers.
```
AP + PAᵀ − PCᵀR⁻¹CP + Q = 0
```
Let A = 0, C = I.
```
−PR⁻¹P + Q = 0
```
Solve for P. P, Q, and R are all diagonal, so this can be solved element-wise.
```
−pr⁻¹p + q = 0
−pr⁻¹p = −q
pr⁻¹p = q
p²r⁻¹ = q
p² = qr
p = sqrt(qr)
```
Now solve for the Kalman gain.
```
K = PCᵀ(CPCᵀ + R)⁻¹
K = P(P + R)⁻¹
k = p(p + r)⁻¹
k = sqrt(qr)(sqrt(qr) + r)⁻¹
k = sqrt(qr)/(sqrt(qr) + r)
```
Multiply by sqrt(q/r)/sqrt(q/r).
```
k = q/(q + r sqrt(q/r))
k = q/(q + sqrt(qr²/r))
k = q/(q + sqrt(qr))
```
### Corner cases
For q = 0 and r ≠ 0,
```
k = 0/(0 + sqrt(0))
k = 0/0
```
Apply L'Hôpital's rule to k with respect to q.
```
k = 1/(1 + r/(2sqrt(qr)))
k = 2sqrt(qr)/(2sqrt(qr) + r)
k = 2sqrt(0)/(2sqrt(0) + r)
k = 0/r
k = 0
```
For q ≠ 0 and r = 0,
```
k = q / (q + sqrt(0))
k = q / q
k = 1
```

414
wpimath/src/main/java/edu/wpi/first/math/estimator/DifferentialDrivePoseEstimator.java
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@@ -4,168 +4,87 @@
package edu.wpi.first.math.estimator;
import edu.wpi.first.math.MatBuilder;
import edu.wpi.first.math.MathUtil;
import edu.wpi.first.math.Matrix;
import edu.wpi.first.math.Nat;
import edu.wpi.first.math.StateSpaceUtil;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.geometry.Twist2d;
import edu.wpi.first.math.interpolation.Interpolatable;
import edu.wpi.first.math.interpolation.TimeInterpolatableBuffer;
import edu.wpi.first.math.kinematics.DifferentialDriveWheelSpeeds;
import edu.wpi.first.math.kinematics.DifferentialDriveKinematics;
import edu.wpi.first.math.kinematics.DifferentialDriveOdometry;
import edu.wpi.first.math.numbers.N1;
import edu.wpi.first.math.numbers.N3;
import edu.wpi.first.math.numbers.N5;
import edu.wpi.first.util.WPIUtilJNI;
import java.util.function.BiConsumer;
import java.util.Map;
import java.util.Objects;
/**
* This class wraps an {@link edu.wpi.first.math.estimator.UnscentedKalmanFilter Unscented Kalman
* Filter} to fuse latency-compensated vision measurements with differential drive encoder
* measurements. It will correct for noisy vision measurements and encoder drift. It is intended to
* be an easy drop-in for {@link edu.wpi.first.math.kinematics.DifferentialDriveOdometry}; in fact,
* if you never call {@link DifferentialDrivePoseEstimator#addVisionMeasurement} and only call
* {@link DifferentialDrivePoseEstimator#update} then this will behave exactly the same as
* This class wraps {@link DifferentialDriveOdometry Differential Drive Odometry} to fuse
* latency-compensated vision measurements with differential drive encoder measurements. It is
* intended to be a drop-in replacement for {@link DifferentialDriveOdometry}; in fact, if you never
* call {@link DifferentialDrivePoseEstimator#addVisionMeasurement} and only call {@link
* DifferentialDrivePoseEstimator#update} then this will behave exactly the same as
* DifferentialDriveOdometry.
*
* <p>{@link DifferentialDrivePoseEstimator#update} should be called every robot loop (if your robot
* loops are faster than the default of 20 ms then you should change the {@link
* DifferentialDrivePoseEstimator#DifferentialDrivePoseEstimator(Rotation2d, double, double, Pose2d,
* Matrix, Matrix, Matrix, double) nominal delta time}.) {@link
* DifferentialDrivePoseEstimator#addVisionMeasurement} can be called as infrequently as you want;
* if you never call it then this class will behave exactly like regular encoder odometry.
* <p>{@link DifferentialDrivePoseEstimator#update} should be called every robot loop.
*
* <p>The state-space system used internally has the following states (x), inputs (u), and outputs
* (y):
* <p>{@link DifferentialDrivePoseEstimator#addVisionMeasurement} can be called as infrequently as
* you want; if you never call it then this class will behave exactly like regular encoder odometry.
*
* <p><strong> x = [x, y, theta, dist_l, dist_r]ᵀ </strong> in the field coordinate system
* containing x position, y position, heading, left encoder distance, and right encoder distance.
* <p>The state-space system used internally has the following states (x), and outputs (y):
*
* <p><strong> u = [v_x, v_y, omega]ᵀ </strong> containing x velocity, y velocity, and angular rate
* in the field coordinate system.
*
* <p>NB: Using velocities make things considerably easier, because it means that teams don't have
* to worry about getting an accurate model. Basically, we suspect that it's easier for teams to get
* good encoder data than it is for them to perform system identification well enough to get a good
* model.
* <p><strong> x = [x, y, theta]ᵀ </strong> in the field coordinate system containing x position, y
* position, and heading.
*
* <p><strong> y = [x, y, theta]ᵀ </strong> from vision containing x position, y position, and
* heading; or <strong>y = [dist_l, dist_r, theta] </strong> containing left encoder position, right
* encoder position, and gyro heading.
* heading.
*/
public class DifferentialDrivePoseEstimator {
final UnscentedKalmanFilter<N5, N3, N3> m_observer; // Package-private to allow for unit testing
private final BiConsumer<Matrix<N3, N1>, Matrix<N3, N1>> m_visionCorrect;
private final TimeInterpolatableBuffer<Pose2d> m_poseBuffer;
private final DifferentialDriveKinematics m_kinematics;
private final DifferentialDriveOdometry m_odometry;
private final Matrix<N3, N1> m_q = new Matrix<>(Nat.N3(), Nat.N1());
private Matrix<N3, N3> m_visionK = new Matrix<>(Nat.N3(), Nat.N3());
private final double m_nominalDt; // Seconds
private double m_prevTimeSeconds = -1.0;
private Rotation2d m_gyroOffset;
private Rotation2d m_previousAngle;
private Matrix<N3, N3> m_visionContR;
private final TimeInterpolatableBuffer<InterpolationRecord> m_poseBuffer =
TimeInterpolatableBuffer.createBuffer(1.5);
/**
* Constructs a DifferentialDrivePoseEstimator.
*
* @param kinematics A correctly-configured kinematics object for your drivetrain.
* @param gyroAngle The current gyro angle.
* @param leftDistanceMeters The distance traveled by the left encoder.
* @param rightDistanceMeters The distance traveled by the right encoder.
* @param initialPoseMeters The starting pose estimate.
* @param stateStdDevs Standard deviations of model states. Increase these numbers to trust your
* model's state estimates less. This matrix is in the form [x, y, theta, dist_l, dist_r]ᵀ,
* with units in meters and radians.
* @param localMeasurementStdDevs Standard deviations of the encoder and gyro measurements.
* Increase these numbers to trust sensor readings from encoders and gyros less. This matrix
* is in the form [dist_l, dist_r, theta]ᵀ, with units in meters and radians.
* model's state estimates less. This matrix is in the form [x, y, theta]ᵀ, with units in
* meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision measurements. Increase these
* numbers to trust global measurements from vision less. This matrix is in the form [x, y,
* theta]ᵀ, with units in meters and radians.
*/
public DifferentialDrivePoseEstimator(
DifferentialDriveKinematics kinematics,
Rotation2d gyroAngle,
double leftDistanceMeters,
double rightDistanceMeters,
Pose2d initialPoseMeters,
Matrix<N5, N1> stateStdDevs,
Matrix<N3, N1> localMeasurementStdDevs,
Matrix<N3, N1> stateStdDevs,
Matrix<N3, N1> visionMeasurementStdDevs) {
this(
gyroAngle,
leftDistanceMeters,
rightDistanceMeters,
initialPoseMeters,
stateStdDevs,
localMeasurementStdDevs,
visionMeasurementStdDevs,
0.02);
}
m_kinematics = kinematics;
m_odometry =
new DifferentialDriveOdometry(
gyroAngle, leftDistanceMeters, rightDistanceMeters, initialPoseMeters);
/**
* Constructs a DifferentialDrivePoseEstimator.
*
* @param gyroAngle The current gyro angle.
* @param leftDistanceMeters The distance traveled by the left encoder.
* @param rightDistanceMeters The distance traveled by the right encoder.
* @param initialPoseMeters The starting pose estimate.
* @param stateStdDevs Standard deviations of model states. Increase these numbers to trust your
* model's state estimates less. This matrix is in the form [x, y, theta, dist_l, dist_r]ᵀ,
* with units in meters and radians.
* @param localMeasurementStdDevs Standard deviations of the encoder and gyro measurements.
* Increase these numbers to trust sensor readings from encoders and gyros less. This matrix
* is in the form [dist_l, dist_r, theta]ᵀ, with units in meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision measurements. Increase these
* numbers to trust global measurements from vision less. This matrix is in the form [x, y,
* theta]ᵀ, with units in meters and radians.
* @param nominalDtSeconds The time in seconds between each robot loop.
*/
public DifferentialDrivePoseEstimator(
Rotation2d gyroAngle,
double leftDistanceMeters,
double rightDistanceMeters,
Pose2d initialPoseMeters,
Matrix<N5, N1> stateStdDevs,
Matrix<N3, N1> localMeasurementStdDevs,
Matrix<N3, N1> visionMeasurementStdDevs,
double nominalDtSeconds) {
m_nominalDt = nominalDtSeconds;
m_observer =
new UnscentedKalmanFilter<>(
Nat.N5(),
Nat.N3(),
this::f,
(x, u) -> VecBuilder.fill(x.get(3, 0), x.get(4, 0), x.get(2, 0)),
stateStdDevs,
localMeasurementStdDevs,
AngleStatistics.angleMean(2),
AngleStatistics.angleMean(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleAdd(2),
m_nominalDt);
m_poseBuffer = TimeInterpolatableBuffer.createBuffer(1.5);
for (int i = 0; i < 3; ++i) {
m_q.set(i, 0, stateStdDevs.get(i, 0) * stateStdDevs.get(i, 0));
}
// Initialize vision R
setVisionMeasurementStdDevs(visionMeasurementStdDevs);
m_visionCorrect =
(u, y) ->
m_observer.correct(
Nat.N3(),
u,
y,
(x, u1) -> new Matrix<>(x.getStorage().extractMatrix(0, 3, 0, 1)),
m_visionContR,
AngleStatistics.angleMean(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleAdd(2));
m_gyroOffset = initialPoseMeters.getRotation().minus(gyroAngle);
m_previousAngle = initialPoseMeters.getRotation();
m_observer.setXhat(fillStateVector(initialPoseMeters, leftDistanceMeters, rightDistanceMeters));
}
/**
@@ -178,42 +97,21 @@ public class DifferentialDrivePoseEstimator {
* theta]ᵀ, with units in meters and radians.
*/
public void setVisionMeasurementStdDevs(Matrix<N3, N1> visionMeasurementStdDevs) {
m_visionContR = StateSpaceUtil.makeCovarianceMatrix(Nat.N3(), visionMeasurementStdDevs);
}
var r = new double[3];
for (int i = 0; i < 3; ++i) {
r[i] = visionMeasurementStdDevs.get(i, 0) * visionMeasurementStdDevs.get(i, 0);
}
private Matrix<N5, N1> f(Matrix<N5, N1> x, Matrix<N3, N1> u) {
// Apply a rotation matrix. Note that we do *not* add x--Runge-Kutta does that for us.
var theta = x.get(2, 0);
var toFieldRotation =
new MatBuilder<>(Nat.N5(), Nat.N5())
.fill(
Math.cos(theta),
-Math.sin(theta),
0,
0,
0,
Math.sin(theta),
Math.cos(theta),
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
1,
0,
0,
0,
0,
0,
1);
return toFieldRotation.times(
VecBuilder.fill(u.get(0, 0), u.get(1, 0), u.get(2, 0), u.get(0, 0), u.get(1, 0)));
// Solve for closed form Kalman gain for continuous Kalman filter with A = 0
// and C = I. See wpimath/algorithms.md.
for (int row = 0; row < 3; ++row) {
if (m_q.get(row, 0) == 0.0) {
m_visionK.set(row, row, 0.0);
} else {
m_visionK.set(
row, row, m_q.get(row, 0) / (m_q.get(row, 0) + Math.sqrt(m_q.get(row, 0) * r[row])));
}
}
}
/**
@@ -233,30 +131,22 @@ public class DifferentialDrivePoseEstimator {
double rightPositionMeters,
Pose2d poseMeters) {
// Reset state estimate and error covariance
m_observer.reset();
m_odometry.resetPosition(gyroAngle, leftPositionMeters, rightPositionMeters, poseMeters);
m_poseBuffer.clear();
m_observer.setXhat(fillStateVector(poseMeters, leftPositionMeters, rightPositionMeters));
m_prevTimeSeconds = -1;
m_gyroOffset = getEstimatedPosition().getRotation().minus(gyroAngle);
m_previousAngle = poseMeters.getRotation();
}
/**
* Gets the pose of the robot at the current time as estimated by the Unscented Kalman Filter.
* Gets the estimated robot pose.
*
* @return The estimated robot pose in meters.
*/
public Pose2d getEstimatedPosition() {
return new Pose2d(
m_observer.getXhat(0), m_observer.getXhat(1), new Rotation2d(m_observer.getXhat(2)));
return m_odometry.getPoseMeters();
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct the odometry pose
* estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the odometry pose estimate
* while still accounting for measurement noise.
*
* <p>This method can be called as infrequently as you want, as long as you are calling {@link
* DifferentialDrivePoseEstimator#update} every loop.
@@ -271,21 +161,49 @@ public class DifferentialDrivePoseEstimator {
* DifferentialDrivePoseEstimator#updateWithTime} then you must use a timestamp with an epoch
* since FPGA startup (i.e. the epoch of this timestamp is the same epoch as
* Timer.getFPGATimestamp.) This means that you should use Timer.getFPGATimestamp as your time
* source in this case.
* source or sync the epochs.
*/
public void addVisionMeasurement(Pose2d visionRobotPoseMeters, double timestampSeconds) {
// Step 1: Get the pose odometry measured at the moment the vision measurement was made.
var sample = m_poseBuffer.getSample(timestampSeconds);
if (sample.isPresent()) {
m_visionCorrect.accept(
new MatBuilder<>(Nat.N3(), Nat.N1()).fill(0.0, 0.0, 0.0),
StateSpaceUtil.poseTo3dVector(
getEstimatedPosition().transformBy(visionRobotPoseMeters.minus(sample.get()))));
if (sample.isEmpty()) {
return;
}
// Step 2: Measure the twist between the odometry pose and the vision pose.
var twist = sample.get().poseMeters.log(visionRobotPoseMeters);
// Step 3: We should not trust the twist entirely, so instead we scale this twist by a Kalman
// gain matrix representing how much we trust vision measurements compared to our current pose.
var k_times_twist = m_visionK.times(VecBuilder.fill(twist.dx, twist.dy, twist.dtheta));
// Step 4: Convert back to Twist2d.
var scaledTwist =
new Twist2d(k_times_twist.get(0, 0), k_times_twist.get(1, 0), k_times_twist.get(2, 0));
// Step 5: Reset Odometry to state at sample with vision adjustment.
m_odometry.resetPosition(
sample.get().gyroAngle,
sample.get().leftMeters,
sample.get().rightMeters,
sample.get().poseMeters.exp(scaledTwist));
// Step 6: Replay odometry inputs between sample time and latest recorded sample to update the
// pose buffer and correct odometry.
for (Map.Entry<Double, InterpolationRecord> entry :
m_poseBuffer.getInternalBuffer().tailMap(timestampSeconds).entrySet()) {
updateWithTime(
entry.getKey(),
entry.getValue().gyroAngle,
entry.getValue().leftMeters,
entry.getValue().rightMeters);
}
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct the odometry pose
* estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the odometry pose estimate
* while still accounting for measurement noise.
*
* <p>This method can be called as infrequently as you want, as long as you are calling {@link
* DifferentialDrivePoseEstimator#update} every loop.
@@ -318,77 +236,127 @@ public class DifferentialDrivePoseEstimator {
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder information. Note that this
* should be called every loop.
* Updates the the Kalman Filter using only wheel encoder information. Note that this should be
* called every loop.
*
* @param gyroAngle The current gyro angle.
* @param wheelVelocitiesMetersPerSecond The velocities of the wheels in meters per second.
* @param distanceLeftMeters The total distance travelled by the left wheel in meters since the
* last time you called {@link DifferentialDrivePoseEstimator#resetPosition}.
* @param distanceRightMeters The total distance travelled by the right wheel in meters since the
* last time you called {@link DifferentialDrivePoseEstimator#resetPosition}.
* @param distanceLeftMeters The total distance travelled by the left wheel in meters.
* @param distanceRightMeters The total distance travelled by the right wheel in meters.
* @return The estimated pose of the robot in meters.
*/
public Pose2d update(
Rotation2d gyroAngle,
DifferentialDriveWheelSpeeds wheelVelocitiesMetersPerSecond,
double distanceLeftMeters,
double distanceRightMeters) {
Rotation2d gyroAngle, double distanceLeftMeters, double distanceRightMeters) {
return updateWithTime(
WPIUtilJNI.now() * 1.0e-6,
gyroAngle,
wheelVelocitiesMetersPerSecond,
distanceLeftMeters,
distanceRightMeters);
WPIUtilJNI.now() * 1.0e-6, gyroAngle, distanceLeftMeters, distanceRightMeters);
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder information. Note that this
* should be called every loop.
* Updates the the Kalman Filter using only wheel encoder information. Note that this should be
* called every loop.
*
* @param currentTimeSeconds Time at which this method was called, in seconds.
* @param gyroAngle The current gyro angle.
* @param wheelVelocitiesMetersPerSecond The velocities of the wheels in meters per second.
* @param distanceLeftMeters The total distance travelled by the left wheel in meters since the
* last time you called {@link DifferentialDrivePoseEstimator#resetPosition}.
* @param distanceRightMeters The total distance travelled by the right wheel in meters since the
* last time you called {@link DifferentialDrivePoseEstimator#resetPosition}.
* @param distanceLeftMeters The total distance travelled by the left wheel in meters.
* @param distanceRightMeters The total distance travelled by the right wheel in meters.
* @return The estimated pose of the robot in meters.
*/
public Pose2d updateWithTime(
double currentTimeSeconds,
Rotation2d gyroAngle,
DifferentialDriveWheelSpeeds wheelVelocitiesMetersPerSecond,
double distanceLeftMeters,
double distanceRightMeters) {
double dt = m_prevTimeSeconds >= 0 ? currentTimeSeconds - m_prevTimeSeconds : m_nominalDt;
m_prevTimeSeconds = currentTimeSeconds;
var angle = gyroAngle.plus(m_gyroOffset);
// Diff drive forward kinematics:
// v_c = (v_l + v_r) / 2
var wheelVels = wheelVelocitiesMetersPerSecond;
var u =
VecBuilder.fill(
(wheelVels.leftMetersPerSecond + wheelVels.rightMetersPerSecond) / 2,
0,
angle.minus(m_previousAngle).getRadians() / dt);
m_previousAngle = angle;
var localY = VecBuilder.fill(distanceLeftMeters, distanceRightMeters, angle.getRadians());
m_poseBuffer.addSample(currentTimeSeconds, getEstimatedPosition());
m_observer.predict(u, dt);
m_observer.correct(u, localY);
m_odometry.update(gyroAngle, distanceLeftMeters, distanceRightMeters);
m_poseBuffer.addSample(
currentTimeSeconds,
new InterpolationRecord(
getEstimatedPosition(), gyroAngle, distanceLeftMeters, distanceRightMeters));
return getEstimatedPosition();
}
private static Matrix<N5, N1> fillStateVector(Pose2d pose, double leftDist, double rightDist) {
return VecBuilder.fill(
pose.getTranslation().getX(),
pose.getTranslation().getY(),
pose.getRotation().getRadians(),
leftDist,
rightDist);
/**
* Represents an odometry record. The record contains the inputs provided as well as the pose that
* was observed based on these inputs, as well as the previous record and its inputs.
*/
private class InterpolationRecord implements Interpolatable<InterpolationRecord> {
// The pose observed given the current sensor inputs and the previous pose.
private final Pose2d poseMeters;
// The current gyro angle.
private final Rotation2d gyroAngle;
// The distance traveled by the left encoder.
private final double leftMeters;
// The distance traveled by the right encoder.
private final double rightMeters;
/**
* Constructs an Interpolation Record with the specified parameters.
*
* @param pose The pose observed given the current sensor inputs and the previous pose.
* @param gyro The current gyro angle.
* @param leftMeters The distance traveled by the left encoder.
* @param rightMeters The distanced traveled by the right encoder.
*/
private InterpolationRecord(
Pose2d poseMeters, Rotation2d gyro, double leftMeters, double rightMeters) {
this.poseMeters = poseMeters;
this.gyroAngle = gyro;
this.leftMeters = leftMeters;
this.rightMeters = rightMeters;
}
/**
* Return the interpolated record. This object is assumed to be the starting position, or lower
* bound.
*
* @param endValue The upper bound, or end.
* @param t How far between the lower and upper bound we are. This should be bounded in [0, 1].
* @return The interpolated value.
*/
@Override
public InterpolationRecord interpolate(InterpolationRecord endValue, double t) {
if (t < 0) {
return this;
} else if (t >= 1) {
return endValue;
} else {
// Find the new left distance.
var left_lerp = MathUtil.interpolate(this.leftMeters, endValue.leftMeters, t);
// Find the new right distance.
var right_lerp = MathUtil.interpolate(this.rightMeters, endValue.rightMeters, t);
// Find the new gyro angle.
var gyro_lerp = gyroAngle.interpolate(endValue.gyroAngle, t);
// Create a twist to represent this change based on the interpolated sensor inputs.
Twist2d twist = m_kinematics.toTwist2d(left_lerp - leftMeters, right_lerp - rightMeters);
twist.dtheta = gyro_lerp.minus(gyroAngle).getRadians();
return new InterpolationRecord(poseMeters.exp(twist), gyro_lerp, left_lerp, right_lerp);
}
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (!(obj instanceof InterpolationRecord)) {
return false;
}
InterpolationRecord record = (InterpolationRecord) obj;
return Objects.equals(gyroAngle, record.gyroAngle)
&& Double.compare(leftMeters, record.leftMeters) == 0
&& Double.compare(rightMeters, record.rightMeters) == 0
&& Objects.equals(poseMeters, record.poseMeters);
}
@Override
public int hashCode() {
return Objects.hash(gyroAngle, leftMeters, rightMeters, poseMeters);
}
}
}

408
wpimath/src/main/java/edu/wpi/first/math/estimator/MecanumDrivePoseEstimator.java
View File

@@ -4,181 +4,83 @@
package edu.wpi.first.math.estimator;
import edu.wpi.first.math.MatBuilder;
import edu.wpi.first.math.MathUtil;
import edu.wpi.first.math.Matrix;
import edu.wpi.first.math.Nat;
import edu.wpi.first.math.StateSpaceUtil;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.geometry.Translation2d;
import edu.wpi.first.math.geometry.Twist2d;
import edu.wpi.first.math.interpolation.Interpolatable;
import edu.wpi.first.math.interpolation.TimeInterpolatableBuffer;
import edu.wpi.first.math.kinematics.MecanumDriveKinematics;
import edu.wpi.first.math.kinematics.MecanumDriveOdometry;
import edu.wpi.first.math.kinematics.MecanumDriveWheelPositions;
import edu.wpi.first.math.kinematics.MecanumDriveWheelSpeeds;
import edu.wpi.first.math.numbers.N1;
import edu.wpi.first.math.numbers.N3;
import edu.wpi.first.math.numbers.N5;
import edu.wpi.first.math.numbers.N7;
import edu.wpi.first.util.WPIUtilJNI;
import java.util.function.BiConsumer;
import java.util.Map;
import java.util.Objects;
/**
* This class wraps an {@link UnscentedKalmanFilter Unscented Kalman Filter} to fuse
* latency-compensated vision measurements with mecanum drive encoder velocity measurements. It will
* correct for noisy measurements and encoder drift. It is intended to be an easy but more accurate
* drop-in for {@link edu.wpi.first.math.kinematics.MecanumDriveOdometry}.
* This class wraps {@link MecanumDriveOdometry Mecanum Drive Odometry} to fuse latency-compensated
* vision measurements with mecanum drive encoder distance measurements. It will correct for noisy
* measurements and encoder drift. It is intended to be a drop-in replacement for {@link
* edu.wpi.first.math.kinematics.MecanumDriveOdometry}.
*
* <p>{@link MecanumDrivePoseEstimator#update} should be called every robot loop. If your loops are
* faster or slower than the default of 20 ms, then you should change the nominal delta time using
* the secondary constructor: {@link MecanumDrivePoseEstimator#MecanumDrivePoseEstimator(Rotation2d,
* MecanumDriveWheelPositions, Pose2d, MecanumDriveKinematics, Matrix, Matrix, Matrix, double)}.
* <p>{@link MecanumDrivePoseEstimator#update} should be called every robot loop.
*
* <p>{@link MecanumDrivePoseEstimator#addVisionMeasurement} can be called as infrequently as you
* want; if you never call it, then this class will behave mostly like regular encoder odometry.
*
* <p>The state-space system used internally has the following states (x), inputs (u), and outputs
* (y):
* <p>The state-space system used internally has the following states (x) and outputs (y):
*
* <p><strong> x = [x, y, theta, s_fl, s_fr, s_rl, s_rr]ᵀ </strong> in the field coordinate system
* containing x position, y position, and heading, followed by the distance driven by the front
* left, front right, rear left, and rear right wheels.
*
* <p><strong> u = [v_x, v_y, omega, v_fl, v_fr, v_rl, v_rr]ᵀ </strong> containing x velocity, y
* velocity, and angular rate in the field coordinate system, followed by the velocity of the front
* left, front right, rear left, and rear right wheels.
* <p><strong> x = [x, y, theta]ᵀ </strong> in the field coordinate system containing x position, y
* position, and heading, followed by the distance driven by the front left, front right, rear left,
* and rear right wheels.
*
* <p><strong> y = [x, y, theta]ᵀ </strong> from vision containing x position, y position, and
* heading; or <strong> y = [theta, s_fl, s_fr, s_rl, s_rr]ᵀ </strong> containing gyro heading,
* followed by the distance driven by the front left, front right, rear left, and rear right wheels.
* heading.
*/
public class MecanumDrivePoseEstimator {
private final UnscentedKalmanFilter<N7, N7, N5> m_observer;
private final MecanumDriveKinematics m_kinematics;
private final BiConsumer<Matrix<N7, N1>, Matrix<N3, N1>> m_visionCorrect;
private final TimeInterpolatableBuffer<Pose2d> m_poseBuffer;
private final MecanumDriveOdometry m_odometry;
private final Matrix<N3, N1> m_q = new Matrix<>(Nat.N3(), Nat.N1());
private Matrix<N3, N3> m_visionK = new Matrix<>(Nat.N3(), Nat.N3());
private final double m_nominalDt; // Seconds
private double m_prevTimeSeconds = -1.0;
private Rotation2d m_gyroOffset;
private Rotation2d m_previousAngle;
private Matrix<N3, N3> m_visionContR;
private final TimeInterpolatableBuffer<InterpolationRecord> m_poseBuffer =
TimeInterpolatableBuffer.createBuffer(1.5);
/**
* Constructs a MecanumDrivePoseEstimator.
*
* @param kinematics A correctly-configured kinematics object for your drivetrain.
* @param gyroAngle The current gyro angle.
* @param wheelPositions The distances driven by each wheel.
* @param initialPoseMeters The starting pose estimate.
* @param kinematics A correctly-configured kinematics object for your drivetrain.
* @param stateStdDevs Standard deviations of model states. Increase these numbers to trust your
* model's state estimates less. This matrix is in the form [x, y, theta, s_fl, s_fr, s_rl,
* s_rr]ᵀ, with units in meters and radians, followed by meters.
* @param localMeasurementStdDevs Standard deviation of the gyro measurement. Increase this number
* to trust sensor readings from the gyro less. This matrix is in the form [theta, s_fl, s_fr,
* s_rl, s_rr], with units in radians, followed by meters.
* model's state estimates less. This matrix is in the form [x, y, theta]ᵀ, with units in
* meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision measurements. Increase these
* numbers to trust global measurements from vision less. This matrix is in the form [x, y,
* theta]ᵀ, with units in meters and radians.
*/
public MecanumDrivePoseEstimator(
MecanumDriveKinematics kinematics,
Rotation2d gyroAngle,
MecanumDriveWheelPositions wheelPositions,
Pose2d initialPoseMeters,
MecanumDriveKinematics kinematics,
Matrix<N7, N1> stateStdDevs,
Matrix<N5, N1> localMeasurementStdDevs,
Matrix<N3, N1> stateStdDevs,
Matrix<N3, N1> visionMeasurementStdDevs) {
this(
gyroAngle,
wheelPositions,
initialPoseMeters,
kinematics,
stateStdDevs,
localMeasurementStdDevs,
visionMeasurementStdDevs,
0.02);
}
/**
* Constructs a MecanumDrivePoseEstimator.
*
* @param gyroAngle The current gyro angle.
* @param wheelPositions The distances driven by each wheel.
* @param initialPoseMeters The starting pose estimate.
* @param kinematics A correctly-configured kinematics object for your drivetrain.
* @param stateStdDevs Standard deviations of model states. Increase these numbers to trust your
* model's state estimates less. This matrix is in the form [x, y, theta, s_fl, s_fr, s_rl,
* s_rr]ᵀ, with units in meters and radians, followed by meters.
* @param localMeasurementStdDevs Standard deviation of the gyro measurement. Increase this number
* to trust sensor readings from the gyro less. This matrix is in the form [theta, s_fl, s_fr,
* s_rl, s_rr], with units in radians, followed by meters.
* @param visionMeasurementStdDevs Standard deviations of the vision measurements. Increase these
* numbers to trust global measurements from vision less. This matrix is in the form [x, y,
* theta]ᵀ, with units in meters and radians.
* @param nominalDtSeconds The time in seconds between each robot loop.
*/
public MecanumDrivePoseEstimator(
Rotation2d gyroAngle,
MecanumDriveWheelPositions wheelPositions,
Pose2d initialPoseMeters,
MecanumDriveKinematics kinematics,
Matrix<N7, N1> stateStdDevs,
Matrix<N5, N1> localMeasurementStdDevs,
Matrix<N3, N1> visionMeasurementStdDevs,
double nominalDtSeconds) {
m_nominalDt = nominalDtSeconds;
m_observer =
new UnscentedKalmanFilter<>(
Nat.N7(),
Nat.N5(),
(x, u) -> u,
(x, u) -> x.block(Nat.N5(), Nat.N1(), 2, 0),
stateStdDevs,
localMeasurementStdDevs,
AngleStatistics.angleMean(2),
AngleStatistics.angleMean(0),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(0),
AngleStatistics.angleAdd(2),
m_nominalDt);
m_kinematics = kinematics;
m_poseBuffer = TimeInterpolatableBuffer.createBuffer(1.5);
m_odometry = new MecanumDriveOdometry(kinematics, gyroAngle, wheelPositions, initialPoseMeters);
for (int i = 0; i < 3; ++i) {
m_q.set(i, 0, stateStdDevs.get(i, 0) * stateStdDevs.get(i, 0));
}
// Initialize vision R
setVisionMeasurementStdDevs(visionMeasurementStdDevs);
m_visionCorrect =
(u, y) ->
m_observer.correct(
Nat.N3(),
u,
y,
(x, u1) -> x.block(Nat.N3(), Nat.N1(), 0, 0),
m_visionContR,
AngleStatistics.angleMean(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleAdd(2));
m_gyroOffset = initialPoseMeters.getRotation().minus(gyroAngle);
m_previousAngle = initialPoseMeters.getRotation();
var poseVec = StateSpaceUtil.poseTo3dVector(initialPoseMeters);
var xhat =
VecBuilder.fill(
poseVec.get(0, 0),
poseVec.get(1, 0),
poseVec.get(2, 0),
wheelPositions.frontLeftMeters,
wheelPositions.frontRightMeters,
wheelPositions.rearLeftMeters,
wheelPositions.rearRightMeters);
m_observer.setXhat(xhat);
}
/**
@@ -191,7 +93,21 @@ public class MecanumDrivePoseEstimator {
* theta]ᵀ, with units in meters and radians.
*/
public void setVisionMeasurementStdDevs(Matrix<N3, N1> visionMeasurementStdDevs) {
m_visionContR = StateSpaceUtil.makeCovarianceMatrix(Nat.N3(), visionMeasurementStdDevs);
var r = new double[3];
for (int i = 0; i < 3; ++i) {
r[i] = visionMeasurementStdDevs.get(i, 0) * visionMeasurementStdDevs.get(i, 0);
}
// Solve for closed form Kalman gain for continuous Kalman filter with A = 0
// and C = I. See wpimath/algorithms.md.
for (int row = 0; row < 3; ++row) {
if (m_q.get(row, 0) == 0.0) {
m_visionK.set(row, row, 0.0);
} else {
m_visionK.set(
row, row, m_q.get(row, 0) / (m_q.get(row, 0) + Math.sqrt(m_q.get(row, 0) * r[row])));
}
}
}
/**
@@ -207,41 +123,22 @@ public class MecanumDrivePoseEstimator {
public void resetPosition(
Rotation2d gyroAngle, MecanumDriveWheelPositions wheelPositions, Pose2d poseMeters) {
// Reset state estimate and error covariance
m_observer.reset();
m_odometry.resetPosition(gyroAngle, wheelPositions, poseMeters);
m_poseBuffer.clear();
var poseVec = StateSpaceUtil.poseTo3dVector(poseMeters);
var xhat =
VecBuilder.fill(
poseVec.get(0, 0),
poseVec.get(1, 0),
poseVec.get(2, 0),
wheelPositions.frontLeftMeters,
wheelPositions.frontRightMeters,
wheelPositions.rearLeftMeters,
wheelPositions.rearRightMeters);
m_observer.setXhat(xhat);
m_prevTimeSeconds = -1;
m_gyroOffset = getEstimatedPosition().getRotation().minus(gyroAngle);
m_previousAngle = poseMeters.getRotation();
}
/**
* Gets the pose of the robot at the current time as estimated by the Unscented Kalman Filter.
* Gets the estimated robot pose.
*
* @return The estimated robot pose in meters.
*/
public Pose2d getEstimatedPosition() {
return new Pose2d(
m_observer.getXhat(0), m_observer.getXhat(1), new Rotation2d(m_observer.getXhat(2)));
return m_odometry.getPoseMeters();
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct the odometry pose
* estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the odometry pose estimate
* while still accounting for measurement noise.
*
* <p>This method can be called as infrequently as you want, as long as you are calling {@link
* MecanumDrivePoseEstimator#update} every loop.
@@ -258,18 +155,41 @@ public class MecanumDrivePoseEstimator {
* Timer.getFPGATimestamp as your time source or sync the epochs.
*/
public void addVisionMeasurement(Pose2d visionRobotPoseMeters, double timestampSeconds) {
// Step 1: Get the pose odometry measured at the moment the vision measurement was made.
var sample = m_poseBuffer.getSample(timestampSeconds);
if (sample.isPresent()) {
m_visionCorrect.accept(
new MatBuilder<>(Nat.N7(), Nat.N1()).fill(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
StateSpaceUtil.poseTo3dVector(
getEstimatedPosition().transformBy(visionRobotPoseMeters.minus(sample.get()))));
if (sample.isEmpty()) {
return;
}
// Step 2: Measure the twist between the odometry pose and the vision pose.
var twist = sample.get().poseMeters.log(visionRobotPoseMeters);
// Step 3: We should not trust the twist entirely, so instead we scale this twist by a Kalman
// gain matrix representing how much we trust vision measurements compared to our current pose.
var k_times_twist = m_visionK.times(VecBuilder.fill(twist.dx, twist.dy, twist.dtheta));
// Step 4: Convert back to Twist2d.
var scaledTwist =
new Twist2d(k_times_twist.get(0, 0), k_times_twist.get(1, 0), k_times_twist.get(2, 0));
// Step 5: Reset Odometry to state at sample with vision adjustment.
m_odometry.resetPosition(
sample.get().gyroAngle,
sample.get().wheelPositions,
sample.get().poseMeters.exp(scaledTwist));
// Step 6: Replay odometry inputs between sample time and latest recorded sample to update the
// pose buffer and correct odometry.
for (Map.Entry<Double, InterpolationRecord> entry :
m_poseBuffer.getInternalBuffer().tailMap(timestampSeconds).entrySet()) {
updateWithTime(entry.getKey(), entry.getValue().gyroAngle, entry.getValue().wheelPositions);
}
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct the odometry pose
* estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the odometry pose estimate
* while still accounting for measurement noise.
*
* <p>This method can be called as infrequently as you want, as long as you are calling {@link
* MecanumDrivePoseEstimator#update} every loop.
@@ -301,71 +221,141 @@ public class MecanumDrivePoseEstimator {
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder information. This should be
* called every loop, and the correct loop period must be passed into the constructor of this
* class.
* Updates the Kalman Filter using only wheel encoder information. This should be called every
* loop.
*
* @param gyroAngle The current gyro angle.
* @param wheelSpeeds The current speeds of the mecanum drive wheels.
* @param wheelPositions The distances driven by each wheel.
* @return The estimated pose of the robot in meters.
*/
public Pose2d update(
Rotation2d gyroAngle,
MecanumDriveWheelSpeeds wheelSpeeds,
MecanumDriveWheelPositions wheelPositions) {
return updateWithTime(WPIUtilJNI.now() * 1.0e-6, gyroAngle, wheelSpeeds, wheelPositions);
public Pose2d update(Rotation2d gyroAngle, MecanumDriveWheelPositions wheelPositions) {
return updateWithTime(WPIUtilJNI.now() * 1.0e-6, gyroAngle, wheelPositions);
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder information. This should be
* called every loop, and the correct loop period must be passed into the constructor of this
* class.
* Updates the Kalman Filter using only wheel encoder information. This should be called every
* loop.
*
* @param currentTimeSeconds Time at which this method was called, in seconds.
* @param gyroAngle The current gyroscope angle.
* @param wheelSpeeds The current speeds of the mecanum drive wheels.
* @param wheelPositions The distances driven by each wheel.
* @return The estimated pose of the robot in meters.
*/
public Pose2d updateWithTime(
double currentTimeSeconds,
Rotation2d gyroAngle,
MecanumDriveWheelSpeeds wheelSpeeds,
MecanumDriveWheelPositions wheelPositions) {
double dt = m_prevTimeSeconds >= 0 ? currentTimeSeconds - m_prevTimeSeconds : m_nominalDt;
m_prevTimeSeconds = currentTimeSeconds;
double currentTimeSeconds, Rotation2d gyroAngle, MecanumDriveWheelPositions wheelPositions) {
m_odometry.update(gyroAngle, wheelPositions);
var angle = gyroAngle.plus(m_gyroOffset);
var omega = angle.minus(m_previousAngle).getRadians() / dt;
var chassisSpeeds = m_kinematics.toChassisSpeeds(wheelSpeeds);
var fieldRelativeVelocities =
new Translation2d(chassisSpeeds.vxMetersPerSecond, chassisSpeeds.vyMetersPerSecond)
.rotateBy(angle);
var u =
VecBuilder.fill(
fieldRelativeVelocities.getX(),
fieldRelativeVelocities.getY(),
omega,
wheelSpeeds.frontLeftMetersPerSecond,
wheelSpeeds.frontRightMetersPerSecond,
wheelSpeeds.rearLeftMetersPerSecond,
wheelSpeeds.rearRightMetersPerSecond);
m_previousAngle = angle;
var localY =
VecBuilder.fill(
angle.getRadians(),
wheelPositions.frontLeftMeters,
wheelPositions.frontRightMeters,
wheelPositions.rearLeftMeters,
wheelPositions.rearRightMeters);
m_poseBuffer.addSample(currentTimeSeconds, getEstimatedPosition());
m_observer.predict(u, dt);
m_observer.correct(u, localY);
m_poseBuffer.addSample(
currentTimeSeconds,
new InterpolationRecord(
getEstimatedPosition(),
gyroAngle,
new MecanumDriveWheelPositions(
wheelPositions.frontLeftMeters,
wheelPositions.frontRightMeters,
wheelPositions.rearLeftMeters,
wheelPositions.rearRightMeters)));
return getEstimatedPosition();
}
/**
* Represents an odometry record. The record contains the inputs provided as well as the pose that
* was observed based on these inputs, as well as the previous record and its inputs.
*/
private class InterpolationRecord implements Interpolatable<InterpolationRecord> {
// The pose observed given the current sensor inputs and the previous pose.
private final Pose2d poseMeters;
// The current gyro angle.
private final Rotation2d gyroAngle;
// The distances traveled by each wheel encoder.
private final MecanumDriveWheelPositions wheelPositions;
/**
* Constructs an Interpolation Record with the specified parameters.
*
* @param pose The pose observed given the current sensor inputs and the previous pose.
* @param gyro The current gyro angle.
* @param wheelPositions The distances traveled by each wheel encoder.
*/
private InterpolationRecord(
Pose2d poseMeters, Rotation2d gyro, MecanumDriveWheelPositions wheelPositions) {
this.poseMeters = poseMeters;
this.gyroAngle = gyro;
this.wheelPositions = wheelPositions;
}
/**
* Return the interpolated record. This object is assumed to be the starting position, or lower
* bound.
*
* @param endValue The upper bound, or end.
* @param t How far between the lower and upper bound we are. This should be bounded in [0, 1].
* @return The interpolated value.
*/
@Override
public InterpolationRecord interpolate(InterpolationRecord endValue, double t) {
if (t < 0) {
return this;
} else if (t >= 1) {
return endValue;
} else {
// Find the new wheel distances.
var wheels_lerp =
new MecanumDriveWheelPositions(
MathUtil.interpolate(
this.wheelPositions.frontLeftMeters,
endValue.wheelPositions.frontLeftMeters,
t),
MathUtil.interpolate(
this.wheelPositions.frontRightMeters,
endValue.wheelPositions.frontRightMeters,
t),
MathUtil.interpolate(
this.wheelPositions.rearLeftMeters, endValue.wheelPositions.rearLeftMeters, t),
MathUtil.interpolate(
this.wheelPositions.rearRightMeters,
endValue.wheelPositions.rearRightMeters,
t));
// Find the distance travelled between this measurement and the interpolated measurement.
var wheels_delta =
new MecanumDriveWheelPositions(
wheels_lerp.frontLeftMeters - this.wheelPositions.frontLeftMeters,
wheels_lerp.frontRightMeters - this.wheelPositions.frontRightMeters,
wheels_lerp.rearLeftMeters - this.wheelPositions.rearLeftMeters,
wheels_lerp.rearRightMeters - this.wheelPositions.rearRightMeters);
// Find the new gyro angle.
var gyro_lerp = gyroAngle.interpolate(endValue.gyroAngle, t);
// Create a twist to represent this change based on the interpolated sensor inputs.
Twist2d twist = m_kinematics.toTwist2d(wheels_delta);
twist.dtheta = gyro_lerp.minus(gyroAngle).getRadians();
return new InterpolationRecord(poseMeters.exp(twist), gyro_lerp, wheels_lerp);
}
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (!(obj instanceof InterpolationRecord)) {
return false;
}
InterpolationRecord record = (InterpolationRecord) obj;
return Objects.equals(gyroAngle, record.gyroAngle)
&& Objects.equals(wheelPositions, record.wheelPositions)
&& Objects.equals(poseMeters, record.poseMeters);
}
@Override
public int hashCode() {
return Objects.hash(gyroAngle, wheelPositions, poseMeters);
}
}
}

454
wpimath/src/main/java/edu/wpi/first/math/estimator/SwerveDrivePoseEstimator.java
View File

@@ -4,228 +4,83 @@
package edu.wpi.first.math.estimator;
import edu.wpi.first.math.MathUtil;
import edu.wpi.first.math.Matrix;
import edu.wpi.first.math.Nat;
import edu.wpi.first.math.Num;
import edu.wpi.first.math.StateSpaceUtil;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.geometry.Translation2d;
import edu.wpi.first.math.geometry.Twist2d;
import edu.wpi.first.math.interpolation.Interpolatable;
import edu.wpi.first.math.interpolation.TimeInterpolatableBuffer;
import edu.wpi.first.math.kinematics.SwerveDriveKinematics;
import edu.wpi.first.math.kinematics.SwerveDriveOdometry;
import edu.wpi.first.math.kinematics.SwerveModulePosition;
import edu.wpi.first.math.kinematics.SwerveModuleState;
import edu.wpi.first.math.numbers.N1;
import edu.wpi.first.math.numbers.N3;
import edu.wpi.first.util.WPIUtilJNI;
import java.util.function.BiConsumer;
import java.util.Map;
import java.util.Objects;
/**
* This class wraps an {@link UnscentedKalmanFilter Unscented Kalman Filter} to fuse
* latency-compensated vision measurements with swerve drive encoder velocity measurements. It will
* correct for noisy measurements and encoder drift. It is intended to be an easy but more accurate
* drop-in for {@link edu.wpi.first.math.kinematics.SwerveDriveOdometry}.
* This class wraps {@link SwerveDriveOdometry Swerve Drive Odometry} to fuse latency-compensated
* vision measurements with swerve drive encoder distance measurements. It is intended to be a
* drop-in replacement for {@link edu.wpi.first.math.kinematics.SwerveDriveOdometry}.
*
* <p>The generic arguments to this class define the size of the state, input and output vectors
* used in the underlying {@link UnscentedKalmanFilter Unscented Kalman Filter}. {@link Num States}
* must be equal to the module count + 3. {@link Num Inputs} must be equal to the module count + 3.
* {@link Num Outputs} must be equal to the module count + 1.
*
* <p>{@link SwerveDrivePoseEstimator#update} should be called every robot loop. If your loops are
* faster or slower than the default of 20 ms, then you should change the nominal delta time using
* the secondary constructor: {@link SwerveDrivePoseEstimator#SwerveDrivePoseEstimator(Nat, Nat,
* Nat, Rotation2d, SwerveModulePosition[], Pose2d, SwerveDriveKinematics, Matrix, Matrix, Matrix,
* double)}.
* <p>{@link SwerveDrivePoseEstimator#update} should be called every robot loop.
*
* <p>{@link SwerveDrivePoseEstimator#addVisionMeasurement} can be called as infrequently as you
* want; if you never call it, then this class will behave mostly like regular encoder odometry.
* want; if you never call it, then this class will behave as regular encoder odometry.
*
* <p>The state-space system used internally has the following states (x), inputs (u), and outputs
* (y):
* <p>The state-space system used internally has the following states (x) and outputs (y):
*
* <p><strong> x = [x, y, theta, s_0, ..., s_n]ᵀ </strong> in the field coordinate system containing
* x position, y position, and heading, followed by the distance travelled by each wheel.
*
* <p><strong> u = [v_x, v_y, omega, v_0, ... v_n]ᵀ </strong> containing x velocity, y velocity, and
* angular rate in the field coordinate system, followed by the velocity measured at each wheel.
* <p><strong> x = [x, y, theta]ᵀ </strong> in the field coordinate system containing x position, y
* position, and heading.
*
* <p><strong> y = [x, y, theta]ᵀ </strong> from vision containing x position, y position, and
* heading; or <strong> y = [theta, s_0, ..., s_n]ᵀ </strong> containing gyro heading, followed by
* the distance travelled by each wheel.
* heading.
*/
public class SwerveDrivePoseEstimator<States extends Num, Inputs extends Num, Outputs extends Num> {
private final UnscentedKalmanFilter<States, Inputs, Outputs> m_observer;
public class SwerveDrivePoseEstimator {
private final SwerveDriveKinematics m_kinematics;
private final BiConsumer<Matrix<Inputs, N1>, Matrix<N3, N1>> m_visionCorrect;
private final TimeInterpolatableBuffer<Pose2d> m_poseBuffer;
private final SwerveDriveOdometry m_odometry;
private final Matrix<N3, N1> m_q = new Matrix<>(Nat.N3(), Nat.N1());
private final int m_numModules;
private Matrix<N3, N3> m_visionK = new Matrix<>(Nat.N3(), Nat.N3());
private final Nat<States> m_states;
private final Nat<Inputs> m_inputs;
private final Nat<Outputs> m_outputs;
private final double m_nominalDt; // Seconds
private double m_prevTimeSeconds = -1.0;
private Rotation2d m_gyroOffset;
private Rotation2d m_previousAngle;
private Matrix<N3, N3> m_visionContR;
private final TimeInterpolatableBuffer<InterpolationRecord> m_poseBuffer =
TimeInterpolatableBuffer.createBuffer(1.5);
/**
* Constructs a SwerveDrivePoseEstimator.
*
* @param states The size of the state vector.
* @param inputs The size of the input vector.
* @param outputs The size of the outputs vector.
* @param gyroAngle The current gyro angle.
* @param initialPoseMeters The starting pose estimate.
* @param modulePositions The current distance measurements and rotations of the swerve modules.
* @param kinematics A correctly-configured kinematics object for your drivetrain.
* @param gyroAngle The current gyro angle.
* @param modulePositions The current distance measurements and rotations of the swerve modules.
* @param initialPoseMeters The starting pose estimate.
* @param stateStdDevs Standard deviations of model states. Increase these numbers to trust your
* model's state estimates less. This matrix is in the form [x, y, theta, s_0, ... s_n]ᵀ, with
* units in meters and radians, then meters.
* @param localMeasurementStdDevs Standard deviations of the encoder and gyro measurements.
* Increase these numbers to trust sensor readings from encoders and gyros less. This matrix
* is in the form [theta, s_0, ... s_n], with units in radians followed by meters.
* model's state estimates less. This matrix is in the form [x, y, theta]ᵀ, with units in
* meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision measurements. Increase these
* numbers to trust global measurements from vision less. This matrix is in the form [x, y,
* theta]ᵀ, with units in meters and radians.
*/
public SwerveDrivePoseEstimator(
Nat<States> states,
Nat<Inputs> inputs,
Nat<Outputs> outputs,
SwerveDriveKinematics kinematics,
Rotation2d gyroAngle,
SwerveModulePosition[] modulePositions,
Pose2d initialPoseMeters,
SwerveDriveKinematics kinematics,
Matrix<States, N1> stateStdDevs,
Matrix<Outputs, N1> localMeasurementStdDevs,
Matrix<N3, N1> stateStdDevs,
Matrix<N3, N1> visionMeasurementStdDevs) {
this(
states,
inputs,
outputs,
gyroAngle,
modulePositions,
initialPoseMeters,
kinematics,
stateStdDevs,
localMeasurementStdDevs,
visionMeasurementStdDevs,
0.02);
}
/**
* Constructs a SwerveDrivePoseEstimator.
*
* @param states The size of the state vector.
* @param inputs The size of the input vector.
* @param outputs The size of the outputs vector.
* @param gyroAngle The current gyro angle.
* @param modulePositions The current distance measurements and rotations of the swerve modules.
* @param initialPoseMeters The starting pose estimate.
* @param kinematics A correctly-configured kinematics object for your drivetrain.
* @param stateStdDevs Standard deviations of model states. Increase these numbers to trust your
* model's state estimates less. This matrix is in the form [x, y, theta, s_0, ... s_n]ᵀ, with
* units in meters and radians, then meters.
* @param localMeasurementStdDevs Standard deviations of the encoder and gyro measurements.
* Increase these numbers to trust sensor readings from encoders and gyros less. This matrix
* is in the form [theta, s_0, ... s_n], with units in radians followed by meters.
* @param visionMeasurementStdDevs Standard deviations of the vision measurements. Increase these
* numbers to trust global measurements from vision less. This matrix is in the form [x, y,
* theta]ᵀ, with units in meters and radians.
* @param nominalDtSeconds The time in seconds between each robot loop.
*/
public SwerveDrivePoseEstimator(
Nat<States> states,
Nat<Inputs> inputs,
Nat<Outputs> outputs,
Rotation2d gyroAngle,
SwerveModulePosition[] modulePositions,
Pose2d initialPoseMeters,
SwerveDriveKinematics kinematics,
Matrix<States, N1> stateStdDevs,
Matrix<Outputs, N1> localMeasurementStdDevs,
Matrix<N3, N1> visionMeasurementStdDevs,
double nominalDtSeconds) {
this.m_states = states;
this.m_inputs = inputs;
this.m_outputs = outputs;
if (states.getNum() != modulePositions.length + 3) {
throw new IllegalArgumentException(
String.format(
"Number of states (%s) must be 3 + "
+ "the number of modules provided in constructor (%s).",
states.getNum(), modulePositions.length));
}
if (inputs.getNum() != modulePositions.length + 3) {
throw new IllegalArgumentException(
String.format(
"Number of inputs (%s) must be 3 + "
+ "the number of modules provided in constructor (%s).",
inputs.getNum(), modulePositions.length));
}
if (outputs.getNum() != modulePositions.length + 1) {
throw new IllegalArgumentException(
String.format(
"Number of outputs (%s) must be 3 + "
+ "the number of modules provided in constructor (%s).",
outputs.getNum(), modulePositions.length));
}
m_nominalDt = nominalDtSeconds;
m_observer =
new UnscentedKalmanFilter<>(
states,
outputs,
(x, u) -> u.block(states.getNum(), 1, 0, 0),
(x, u) -> x.block(states.getNum() - 2, 1, 2, 0),
stateStdDevs,
localMeasurementStdDevs,
AngleStatistics.angleMean(2),
AngleStatistics.angleMean(0),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(0),
AngleStatistics.angleAdd(2),
m_nominalDt);
m_kinematics = kinematics;
m_poseBuffer = TimeInterpolatableBuffer.createBuffer(1.5);
m_odometry = new SwerveDriveOdometry(kinematics, gyroAngle, modulePositions, initialPoseMeters);
// Initialize vision R
setVisionMeasurementStdDevs(visionMeasurementStdDevs);
m_visionCorrect =
(u, y) ->
m_observer.correct(
Nat.N3(),
u,
y,
(x, u1) -> x.block(3, 1, 0, 0),
m_visionContR,
AngleStatistics.angleMean(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleAdd(2));
m_gyroOffset = initialPoseMeters.getRotation().minus(gyroAngle);
m_previousAngle = initialPoseMeters.getRotation();
var poseVec = StateSpaceUtil.poseTo3dVector(initialPoseMeters);
Matrix<States, N1> xhat = new Matrix<States, N1>(states, Nat.N1());
xhat.set(0, 0, poseVec.get(0, 0));
xhat.set(1, 0, poseVec.get(1, 0));
xhat.set(2, 0, poseVec.get(2, 0));
for (int index = 3; index < states.getNum(); index++) {
xhat.set(index, 0, modulePositions[index - 3].distanceMeters);
for (int i = 0; i < 3; ++i) {
m_q.set(i, 0, stateStdDevs.get(i, 0) * stateStdDevs.get(i, 0));
}
m_observer.setXhat(xhat);
m_numModules = modulePositions.length;
setVisionMeasurementStdDevs(visionMeasurementStdDevs);
}
/**
@@ -238,7 +93,21 @@ public class SwerveDrivePoseEstimator<States extends Num, Inputs extends Num, Ou
* theta]ᵀ, with units in meters and radians.
*/
public void setVisionMeasurementStdDevs(Matrix<N3, N1> visionMeasurementStdDevs) {
m_visionContR = StateSpaceUtil.makeCovarianceMatrix(Nat.N3(), visionMeasurementStdDevs);
var r = new double[3];
for (int i = 0; i < 3; ++i) {
r[i] = visionMeasurementStdDevs.get(i, 0) * visionMeasurementStdDevs.get(i, 0);
}
// Solve for closed form Kalman gain for continuous Kalman filter with A = 0
// and C = I. See wpimath/algorithms.md.
for (int row = 0; row < 3; ++row) {
if (m_q.get(row, 0) == 0.0) {
m_visionK.set(row, row, 0.0);
} else {
m_visionK.set(
row, row, m_q.get(row, 0) / (m_q.get(row, 0) + Math.sqrt(m_q.get(row, 0) * r[row])));
}
}
}
/**
@@ -254,40 +123,22 @@ public class SwerveDrivePoseEstimator<States extends Num, Inputs extends Num, Ou
public void resetPosition(
Rotation2d gyroAngle, SwerveModulePosition[] modulePositions, Pose2d poseMeters) {
// Reset state estimate and error covariance
m_observer.reset();
m_odometry.resetPosition(gyroAngle, modulePositions, poseMeters);
m_poseBuffer.clear();
var poseVec = StateSpaceUtil.poseTo3dVector(poseMeters);
Matrix<States, N1> xhat = new Matrix<States, N1>(m_states, Nat.N1());
xhat.set(0, 0, poseVec.get(0, 0));
xhat.set(1, 0, poseVec.get(1, 0));
xhat.set(2, 0, poseVec.get(2, 0));
for (int index = 3; index < m_states.getNum(); index++) {
xhat.set(index, 0, modulePositions[index - 3].distanceMeters);
}
m_observer.setXhat(xhat);
m_prevTimeSeconds = -1;
m_gyroOffset = getEstimatedPosition().getRotation().minus(gyroAngle);
m_previousAngle = poseMeters.getRotation();
}
/**
* Gets the pose of the robot at the current time as estimated by the Unscented Kalman Filter.
* Gets the estimated robot pose.
*
* @return The estimated robot pose in meters.
*/
public Pose2d getEstimatedPosition() {
return new Pose2d(
m_observer.getXhat(0), m_observer.getXhat(1), new Rotation2d(m_observer.getXhat(2)));
return m_odometry.getPoseMeters();
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct the odometry pose
* estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the odometry pose estimate
* while still accounting for measurement noise.
*
* <p>This method can be called as infrequently as you want, as long as you are calling {@link
* SwerveDrivePoseEstimator#update} every loop.
@@ -304,18 +155,41 @@ public class SwerveDrivePoseEstimator<States extends Num, Inputs extends Num, Ou
* Timer.getFPGATimestamp as your time source or sync the epochs.
*/
public void addVisionMeasurement(Pose2d visionRobotPoseMeters, double timestampSeconds) {
// Step 1: Get the pose odometry measured at the moment the vision measurement was made.
var sample = m_poseBuffer.getSample(timestampSeconds);
if (sample.isPresent()) {
m_visionCorrect.accept(
new Matrix<Inputs, N1>(m_inputs, Nat.N1()),
StateSpaceUtil.poseTo3dVector(
getEstimatedPosition().transformBy(visionRobotPoseMeters.minus(sample.get()))));
if (sample.isEmpty()) {
return;
}
// Step 2: Measure the twist between the odometry pose and the vision pose.
var twist = sample.get().poseMeters.log(visionRobotPoseMeters);
// Step 3: We should not trust the twist entirely, so instead we scale this twist by a Kalman
// gain matrix representing how much we trust vision measurements compared to our current pose.
var k_times_twist = m_visionK.times(VecBuilder.fill(twist.dx, twist.dy, twist.dtheta));
// Step 4: Convert back to Twist2d.
var scaledTwist =
new Twist2d(k_times_twist.get(0, 0), k_times_twist.get(1, 0), k_times_twist.get(2, 0));
// Step 5: Reset Odometry to state at sample with vision adjustment.
m_odometry.resetPosition(
sample.get().gyroAngle,
sample.get().modulePositions,
sample.get().poseMeters.exp(scaledTwist));
// Step 6: Replay odometry inputs between sample time and latest recorded sample to update the
// pose buffer and correct odometry.
for (Map.Entry<Double, InterpolationRecord> entry :
m_poseBuffer.getInternalBuffer().tailMap(timestampSeconds).entrySet()) {
updateWithTime(entry.getKey(), entry.getValue().gyroAngle, entry.getValue().modulePositions);
}
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct the odometry pose
* estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the odometry pose estimate
* while still accounting for measurement noise.
*
* <p>This method can be called as infrequently as you want, as long as you are calling {@link
* SwerveDrivePoseEstimator#update} every loop.
@@ -347,70 +221,140 @@ public class SwerveDrivePoseEstimator<States extends Num, Inputs extends Num, Ou
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder information. This should be
* called every loop, and the correct loop period must be passed into the constructor of this
* class.
* Updates the Kalman Filter using only wheel encoder information. This should be called every
* loop.
*
* @param gyroAngle The current gyro angle.
* @param moduleStates The current velocities and rotations of the swerve modules.
* @param modulePositions The current distance measurements and rotations of the swerve modules.
* @return The estimated pose of the robot in meters.
*/
public Pose2d update(
Rotation2d gyroAngle,
SwerveModuleState[] moduleStates,
SwerveModulePosition[] modulePositions) {
return updateWithTime(WPIUtilJNI.now() * 1.0e-6, gyroAngle, moduleStates, modulePositions);
public Pose2d update(Rotation2d gyroAngle, SwerveModulePosition[] modulePositions) {
return updateWithTime(WPIUtilJNI.now() * 1.0e-6, gyroAngle, modulePositions);
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder information. This should be
* called every loop, and the correct loop period must be passed into the constructor of this
* class.
* Updates the Kalman Filter using only wheel encoder information. This should be called every
* loop.
*
* @param currentTimeSeconds Time at which this method was called, in seconds.
* @param gyroAngle The current gyroscope angle.
* @param moduleStates The current velocities and rotations of the swerve modules.
* @param modulePositions The current distance measurements and rotations of the swerve modules.
* @return The estimated pose of the robot in meters.
*/
public Pose2d updateWithTime(
double currentTimeSeconds,
Rotation2d gyroAngle,
SwerveModuleState[] moduleStates,
SwerveModulePosition[] modulePositions) {
double dt = m_prevTimeSeconds >= 0 ? currentTimeSeconds - m_prevTimeSeconds : m_nominalDt;
m_prevTimeSeconds = currentTimeSeconds;
var angle = gyroAngle.plus(m_gyroOffset);
var omega = angle.minus(m_previousAngle).getRadians() / dt;
var chassisSpeeds = m_kinematics.toChassisSpeeds(moduleStates);
var fieldRelativeVelocities =
new Translation2d(chassisSpeeds.vxMetersPerSecond, chassisSpeeds.vyMetersPerSecond)
.rotateBy(angle);
var u = new Matrix<Inputs, N1>(m_inputs, Nat.N1());
u.set(0, 0, fieldRelativeVelocities.getX());
u.set(1, 0, fieldRelativeVelocities.getY());
u.set(2, 0, omega);
for (int index = 3; index < m_inputs.getNum(); index++) {
u.set(index, 0, moduleStates[index - 3].speedMetersPerSecond);
double currentTimeSeconds, Rotation2d gyroAngle, SwerveModulePosition[] modulePositions) {
if (modulePositions.length != m_numModules) {
throw new IllegalArgumentException(
"Number of modules is not consistent with number of wheel locations provided in "
+ "constructor");
}
m_previousAngle = angle;
var internalModulePositions = new SwerveModulePosition[m_numModules];
var localY = new Matrix<Outputs, N1>(m_outputs, Nat.N1());
localY.set(0, 0, angle.getRadians());
for (int index = 1; index < m_outputs.getNum(); index++) {
localY.set(index, 0, modulePositions[index - 1].distanceMeters);
for (int i = 0; i < m_numModules; i++) {
internalModulePositions[i] =
new SwerveModulePosition(modulePositions[i].distanceMeters, modulePositions[i].angle);
}
m_poseBuffer.addSample(currentTimeSeconds, getEstimatedPosition());
m_observer.predict(u, dt);
m_observer.correct(u, localY);
m_odometry.update(gyroAngle, internalModulePositions);
m_poseBuffer.addSample(
currentTimeSeconds,
new InterpolationRecord(getEstimatedPosition(), gyroAngle, internalModulePositions));
return getEstimatedPosition();
}
/**
* Represents an odometry record. The record contains the inputs provided as well as the pose that
* was observed based on these inputs, as well as the previous record and its inputs.
*/
private class InterpolationRecord implements Interpolatable<InterpolationRecord> {
// The pose observed given the current sensor inputs and the previous pose.
private final Pose2d poseMeters;
// The current gyro angle.
private final Rotation2d gyroAngle;
// The distances and rotations measured at each module.
private final SwerveModulePosition[] modulePositions;
/**
* Constructs an Interpolation Record with the specified parameters.
*
* @param pose The pose observed given the current sensor inputs and the previous pose.
* @param gyro The current gyro angle.
* @param wheelPositions The distances and rotations measured at each wheel.
*/
private InterpolationRecord(
Pose2d poseMeters, Rotation2d gyro, SwerveModulePosition[] modulePositions) {
this.poseMeters = poseMeters;
this.gyroAngle = gyro;
this.modulePositions = modulePositions;
}
/**
* Return the interpolated record. This object is assumed to be the starting position, or lower
* bound.
*
* @param endValue The upper bound, or end.
* @param t How far between the lower and upper bound we are. This should be bounded in [0, 1].
* @return The interpolated value.
*/
@Override
public InterpolationRecord interpolate(InterpolationRecord endValue, double t) {
if (t < 0) {
return this;
} else if (t >= 1) {
return endValue;
} else {
// Find the new wheel distances.
var modulePositions = new SwerveModulePosition[m_numModules];
// Find the distance travelled between this measurement and the interpolated measurement.
var moduleDeltas = new SwerveModulePosition[m_numModules];
for (int i = 0; i < m_numModules; i++) {
double ds =
MathUtil.interpolate(
this.modulePositions[i].distanceMeters,
endValue.modulePositions[i].distanceMeters,
t);
Rotation2d theta =
this.modulePositions[i].angle.interpolate(endValue.modulePositions[i].angle, t);
modulePositions[i] = new SwerveModulePosition(ds, theta);
moduleDeltas[i] =
new SwerveModulePosition(ds - this.modulePositions[i].distanceMeters, theta);
}
// Find the new gyro angle.
var gyro_lerp = gyroAngle.interpolate(endValue.gyroAngle, t);
// Create a twist to represent this change based on the interpolated sensor inputs.
Twist2d twist = m_kinematics.toTwist2d(moduleDeltas);
twist.dtheta = gyro_lerp.minus(gyroAngle).getRadians();
return new InterpolationRecord(poseMeters.exp(twist), gyro_lerp, modulePositions);
}
}
@Override
public boolean equals(Object obj) {
if (this == obj) {
return true;
}
if (!(obj instanceof InterpolationRecord)) {
return false;
}
InterpolationRecord record = (InterpolationRecord) obj;
return Objects.equals(gyroAngle, record.gyroAngle)
&& Objects.equals(modulePositions, record.modulePositions)
&& Objects.equals(poseMeters, record.poseMeters);
}
@Override
public int hashCode() {
return Objects.hash(gyroAngle, modulePositions, poseMeters);
}
}
}

10
wpimath/src/main/java/edu/wpi/first/math/interpolation/TimeInterpolatableBuffer.java
View File

@@ -134,6 +134,16 @@ public final class TimeInterpolatableBuffer<T> {
}
}
/**
* Grant access to the internal sample buffer. Used in Pose Estimation to replay odometry inputs
* stored within this buffer.
*
* @return The internal sample buffer.
*/
public NavigableMap<Double, T> getInternalBuffer() {
return m_pastSnapshots;
}
public interface InterpolateFunction<T> {
/**
* Return the interpolated value. This object is assumed to be the starting position, or lower

17
wpimath/src/main/java/edu/wpi/first/math/kinematics/DifferentialDriveKinematics.java
View File

@@ -6,6 +6,7 @@ package edu.wpi.first.math.kinematics;
import edu.wpi.first.math.MathSharedStore;
import edu.wpi.first.math.MathUsageId;
import edu.wpi.first.math.geometry.Twist2d;
/**
* Helper class that converts a chassis velocity (dx and dtheta components) to left and right wheel
@@ -57,4 +58,20 @@ public class DifferentialDriveKinematics {
chassisSpeeds.vxMetersPerSecond
+ trackWidthMeters / 2 * chassisSpeeds.omegaRadiansPerSecond);
}
/**
* Performs forward kinematics to return the resulting Twist2d from the given left and right side
* distance deltas. This method is often used for odometry -- determining the robot's position on
* the field using changes in the distance driven by each wheel on the robot.
*
* @param leftDistanceMeters The distance measured by the left side encoder.
* @param rightDistanceMeters The distance measured by the right side encoder.
* @return The resulting Twist2d.
*/
public Twist2d toTwist2d(double leftDistanceMeters, double rightDistanceMeters) {
return new Twist2d(
(leftDistanceMeters + rightDistanceMeters) / 2,
0,
(rightDistanceMeters - leftDistanceMeters) / trackWidthMeters);
}
}

7
wpimath/src/main/java/edu/wpi/first/math/kinematics/SwerveDriveOdometry.java
View File

@@ -134,6 +134,7 @@ public class SwerveDriveOdometry {
moduleDeltas[index] =
new SwerveModulePosition(current.distanceMeters - previous.distanceMeters, current.angle);
previous.distanceMeters = current.distanceMeters;
}
var angle = gyroAngle.plus(m_gyroOffset);
@@ -145,11 +146,7 @@ public class SwerveDriveOdometry {
m_previousAngle = angle;
m_poseMeters = new Pose2d(newPose.getTranslation(), angle);
for (int index = 0; index < m_numModules; index++) {
m_previousModulePositions[index] =
new SwerveModulePosition(
modulePositions[index].distanceMeters, modulePositions[index].angle);
}
return m_poseMeters;
}
}

202
wpimath/src/main/native/cpp/estimator/DifferentialDrivePoseEstimator.cpp
View File

@@ -11,44 +11,64 @@
using namespace frc;
DifferentialDrivePoseEstimator::InterpolationRecord
DifferentialDrivePoseEstimator::InterpolationRecord::Interpolate(
DifferentialDriveKinematics& kinematics, InterpolationRecord endValue,
double i) const {
if (i < 0) {
return *this;
} else if (i > 1) {
return endValue;
} else {
// Find the interpolated left distance.
auto left = wpi::Lerp(this->leftDistance, endValue.leftDistance, i);
// Find the interpolated right distance.
auto right = wpi::Lerp(this->rightDistance, endValue.rightDistance, i);
// Find the new gyro angle.
auto gyro = wpi::Lerp(this->gyroAngle, endValue.gyroAngle, i);
// Create a twist to represent this changed based on the interpolated
// sensor inputs.
auto twist =
kinematics.ToTwist2d(left - leftDistance, right - rightDistance);
twist.dtheta = (gyro - gyroAngle).Radians();
return {pose.Exp(twist), gyro, left, right};
}
}
DifferentialDrivePoseEstimator::DifferentialDrivePoseEstimator(
const Rotation2d& gyroAngle, units::meter_t leftDistance,
units::meter_t rightDistance, const Pose2d& initialPose,
const wpi::array<double, 5>& stateStdDevs,
const wpi::array<double, 3>& localMeasurementStdDevs,
const wpi::array<double, 3>& visionMeasurmentStdDevs,
units::second_t nominalDt)
: m_observer(
&DifferentialDrivePoseEstimator::F,
[](const Vectord<5>& x, const Vectord<3>& u) {
return Vectord<3>{x(3, 0), x(4, 0), x(2, 0)};
},
stateStdDevs, localMeasurementStdDevs, frc::AngleMean<5, 5>(2),
frc::AngleMean<3, 5>(2), frc::AngleResidual<5>(2),
frc::AngleResidual<3>(2), frc::AngleAdd<5>(2), nominalDt),
m_nominalDt(nominalDt) {
SetVisionMeasurementStdDevs(visionMeasurmentStdDevs);
DifferentialDriveKinematics& kinematics, const Rotation2d& gyroAngle,
units::meter_t leftDistance, units::meter_t rightDistance,
const Pose2d& initialPose, const wpi::array<double, 3>& stateStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs)
: m_kinematics{kinematics},
m_odometry{gyroAngle, leftDistance, rightDistance, initialPose} {
for (size_t i = 0; i < 3; ++i) {
m_q[i] = stateStdDevs[i] * stateStdDevs[i];
}
// Create correction mechanism for vision measurements.
m_visionCorrect = [&](const Vectord<3>& u, const Vectord<3>& y) {
m_observer.Correct<3>(
u, y,
[](const Vectord<5>& x, const Vectord<3>&) {
return x.block<3, 1>(0, 0);
},
m_visionContR, frc::AngleMean<3, 5>(2), frc::AngleResidual<3>(2),
frc::AngleResidual<5>(2), frc::AngleAdd<5>(2));
};
m_gyroOffset = initialPose.Rotation() - gyroAngle;
m_previousAngle = initialPose.Rotation();
m_observer.SetXhat(FillStateVector(initialPose, leftDistance, rightDistance));
SetVisionMeasurementStdDevs(visionMeasurementStdDevs);
}
void DifferentialDrivePoseEstimator::SetVisionMeasurementStdDevs(
const wpi::array<double, 3>& visionMeasurmentStdDevs) {
// Create R (covariances) for vision measurements.
m_visionContR = frc::MakeCovMatrix(visionMeasurmentStdDevs);
const wpi::array<double, 3>& visionMeasurementStdDevs) {
wpi::array<double, 3> r{wpi::empty_array};
for (size_t i = 0; i < 3; ++i) {
r[i] = visionMeasurementStdDevs[i] * visionMeasurementStdDevs[i];
}
// Solve for closed form Kalman gain for continuous Kalman filter with A = 0
// and C = I. See wpimath/algorithms.md.
for (size_t row = 0; row < 3; ++row) {
if (m_q[row] == 0.0) {
m_visionK(row, row) = 0.0;
} else {
m_visionK(row, row) =
m_q[row] / (m_q[row] + std::sqrt(m_q[row] * r[row]));
}
}
}
void DifferentialDrivePoseEstimator::ResetPosition(const Rotation2d& gyroAngle,
@@ -56,85 +76,81 @@ void DifferentialDrivePoseEstimator::ResetPosition(const Rotation2d& gyroAngle,
units::meter_t rightDistance,
const Pose2d& pose) {
// Reset state estimate and error covariance
m_observer.Reset();
m_odometry.ResetPosition(gyroAngle, leftDistance, rightDistance, pose);
m_poseBuffer.Clear();
m_observer.SetXhat(FillStateVector(pose, leftDistance, rightDistance));
m_prevTime = -1_s;
m_gyroOffset = GetEstimatedPosition().Rotation() - gyroAngle;
m_previousAngle = pose.Rotation();
}
Pose2d DifferentialDrivePoseEstimator::GetEstimatedPosition() const {
return Pose2d{units::meter_t{m_observer.Xhat(0)},
units::meter_t{m_observer.Xhat(1)},
units::radian_t{m_observer.Xhat(2)}};
return m_odometry.GetPose();
}
void DifferentialDrivePoseEstimator::AddVisionMeasurement(
const Pose2d& visionRobotPose, units::second_t timestamp) {
if (auto sample = m_poseBuffer.Sample(timestamp)) {
m_visionCorrect(Vectord<3>::Zero(),
PoseTo3dVector(GetEstimatedPosition().TransformBy(
visionRobotPose - sample.value())));
// Step 1: Get the estimated pose from when the vision measurement was made.
auto sample = m_poseBuffer.Sample(timestamp);
if (!sample.has_value()) {
return;
}
// Step 2: Measure the twist between the odometry pose and the vision pose.
auto twist = sample.value().pose.Log(visionRobotPose);
// Step 3: We should not trust the twist entirely, so instead we scale this
// twist by a Kalman gain matrix representing how much we trust vision
// measurements compared to our current pose.
frc::Vectord<3> k_times_twist =
m_visionK *
frc::Vectord<3>{twist.dx.value(), twist.dy.value(), twist.dtheta.value()};
// Step 4: Convert back to Twist2d.
Twist2d scaledTwist{units::meter_t{k_times_twist(0)},
units::meter_t{k_times_twist(1)},
units::radian_t{k_times_twist(2)}};
// Step 5: Reset Odometry to state at sample with vision adjustment.
m_odometry.ResetPosition(
sample.value().gyroAngle, sample.value().leftDistance,
sample.value().rightDistance, sample.value().pose.Exp(scaledTwist));
// Step 6: Replay odometry inputs between sample time and latest recorded
// sample to update the pose buffer and correct odometry.
auto internal_buf = m_poseBuffer.GetInternalBuffer();
auto first_newer_record =
std::lower_bound(internal_buf.begin(), internal_buf.end(), timestamp,
[](const auto& pair, auto t) { return t > pair.first; });
for (auto entry = first_newer_record + 1; entry != internal_buf.end();
entry++) {
UpdateWithTime(entry->first, entry->second.gyroAngle,
entry->second.leftDistance, entry->second.rightDistance);
}
}
Pose2d DifferentialDrivePoseEstimator::Update(
const Rotation2d& gyroAngle,
const DifferentialDriveWheelSpeeds& wheelSpeeds,
units::meter_t leftDistance, units::meter_t rightDistance) {
Pose2d DifferentialDrivePoseEstimator::Update(const Rotation2d& gyroAngle,
units::meter_t leftDistance,
units::meter_t rightDistance) {
return UpdateWithTime(units::microsecond_t(wpi::Now()), gyroAngle,
wheelSpeeds, leftDistance, rightDistance);
leftDistance, rightDistance);
}
Pose2d DifferentialDrivePoseEstimator::UpdateWithTime(
units::second_t currentTime, const Rotation2d& gyroAngle,
const DifferentialDriveWheelSpeeds& wheelSpeeds,
units::meter_t leftDistance, units::meter_t rightDistance) {
auto dt = m_prevTime >= 0_s ? currentTime - m_prevTime : m_nominalDt;
m_prevTime = currentTime;
m_odometry.Update(gyroAngle, leftDistance, rightDistance);
auto angle = gyroAngle + m_gyroOffset;
auto omega = (gyroAngle - m_previousAngle).Radians() / dt;
// fmt::print("odo, {}, {}, {}, {}, {}, {}\n",
// gyroAngle.Radians(),
// leftDistance,
// rightDistance,
// GetEstimatedPosition().X(),
// GetEstimatedPosition().Y(),
// GetEstimatedPosition().Rotation().Radians()
// );
auto u = Vectord<3>{(wheelSpeeds.left + wheelSpeeds.right).value() / 2.0, 0.0,
omega.value()};
m_previousAngle = angle;
auto localY = Vectord<3>{leftDistance.value(), rightDistance.value(),
angle.Radians().value()};
m_poseBuffer.AddSample(currentTime, GetEstimatedPosition());
m_observer.Predict(u, dt);
m_observer.Correct(u, localY);
m_poseBuffer.AddSample(currentTime, {GetEstimatedPosition(), gyroAngle,
leftDistance, rightDistance});
return GetEstimatedPosition();
}
Vectord<5> DifferentialDrivePoseEstimator::F(const Vectord<5>& x,
const Vectord<3>& u) {
// Apply a rotation matrix. Note that we do not add x because Runge-Kutta does
// that for us.
auto& theta = x(2);
Matrixd<5, 5> toFieldRotation{
{std::cos(theta), -std::sin(theta), 0.0, 0.0, 0.0},
{std::sin(theta), std::cos(theta), 0.0, 0.0, 0.0},
{0.0, 0.0, 1.0, 0.0, 0.0},
{0.0, 0.0, 0.0, 1.0, 0.0},
{0.0, 0.0, 0.0, 0.0, 1.0}};
return toFieldRotation *
Vectord<5>{u(0, 0), u(1, 0), u(2, 0), u(0, 0), u(1, 0)};
}
Vectord<5> DifferentialDrivePoseEstimator::FillStateVector(
const Pose2d& pose, units::meter_t leftDistance,
units::meter_t rightDistance) {
return Vectord<5>{pose.Translation().X().value(),
pose.Translation().Y().value(),
pose.Rotation().Radians().value(), leftDistance.value(),
rightDistance.value()};
}

215
wpimath/src/main/native/cpp/estimator/MecanumDrivePoseEstimator.cpp
View File

@@ -11,141 +11,152 @@
using namespace frc;
frc::MecanumDrivePoseEstimator::InterpolationRecord
frc::MecanumDrivePoseEstimator::InterpolationRecord::Interpolate(
MecanumDriveKinematics& kinematics, InterpolationRecord endValue,
double i) const {
if (i < 0) {
return *this;
} else if (i > 1) {
return endValue;
} else {
// Find the new wheel distance measurements.
MecanumDriveWheelPositions wheels_lerp{
wpi::Lerp(this->wheelPositions.frontLeft,
endValue.wheelPositions.frontLeft, i),
wpi::Lerp(this->wheelPositions.frontRight,
endValue.wheelPositions.frontRight, i),
wpi::Lerp(this->wheelPositions.rearLeft,
endValue.wheelPositions.rearLeft, i),
wpi::Lerp(this->wheelPositions.rearRight,
endValue.wheelPositions.rearRight, i)};
// Find the distance between this measurement and the
// interpolated measurement.
MecanumDriveWheelPositions wheels_delta{
wheels_lerp.frontLeft - this->wheelPositions.frontLeft,
wheels_lerp.frontRight - this->wheelPositions.frontRight,
wheels_lerp.rearLeft - this->wheelPositions.rearLeft,
wheels_lerp.rearRight - this->wheelPositions.rearRight};
// Find the new gyro angle.
auto gyro = wpi::Lerp(this->gyroAngle, endValue.gyroAngle, i);
// Create a twist to represent this changed based on the interpolated
// sensor inputs.
auto twist = kinematics.ToTwist2d(wheels_delta);
twist.dtheta = (gyro - gyroAngle).Radians();
return {pose.Exp(twist), gyro, wheels_lerp};
}
}
frc::MecanumDrivePoseEstimator::MecanumDrivePoseEstimator(
const Rotation2d& gyroAngle,
MecanumDriveKinematics& kinematics, const Rotation2d& gyroAngle,
const MecanumDriveWheelPositions& wheelPositions, const Pose2d& initialPose,
MecanumDriveKinematics kinematics,
const wpi::array<double, 7>& stateStdDevs,
const wpi::array<double, 5>& localMeasurementStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs,
units::second_t nominalDt)
: m_observer([](const Vectord<7>& x, const Vectord<7>& u) { return u; },
[](const Vectord<7>& x, const Vectord<7>& u) {
return x.block<5, 1>(2, 0);
},
stateStdDevs, localMeasurementStdDevs, frc::AngleMean<7, 7>(2),
frc::AngleMean<5, 7>(0), frc::AngleResidual<7>(2),
frc::AngleResidual<5>(0), frc::AngleAdd<7>(2), nominalDt),
m_kinematics(kinematics),
m_nominalDt(nominalDt) {
const wpi::array<double, 3>& stateStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs)
: m_kinematics{kinematics},
m_odometry{kinematics, gyroAngle, wheelPositions, initialPose} {
for (size_t i = 0; i < 3; ++i) {
m_q[i] = stateStdDevs[i] * stateStdDevs[i];
}
SetVisionMeasurementStdDevs(visionMeasurementStdDevs);
// Create vision correction mechanism.
m_visionCorrect = [&](const Vectord<7>& u, const Vectord<3>& y) {
m_observer.Correct<3>(
u, y,
[](const Vectord<7>& x, const Vectord<7>& u) {
return x.template block<3, 1>(0, 0);
},
m_visionContR, frc::AngleMean<3, 7>(2), frc::AngleResidual<3>(2),
frc::AngleResidual<7>(2), frc::AngleAdd<7>(2));
};
// Set initial state.
auto poseVec = PoseTo3dVector(initialPose);
auto xhat = Vectord<7>{
poseVec(0),
poseVec(1),
poseVec(2),
wheelPositions.frontLeft.value(),
wheelPositions.frontRight.value(),
wheelPositions.rearLeft.value(),
wheelPositions.rearRight.value(),
};
m_observer.SetXhat(xhat);
// Calculate offsets.
m_gyroOffset = initialPose.Rotation() - gyroAngle;
m_previousAngle = initialPose.Rotation();
}
void frc::MecanumDrivePoseEstimator::SetVisionMeasurementStdDevs(
const wpi::array<double, 3>& visionMeasurmentStdDevs) {
// Create R (covariances) for vision measurements.
m_visionContR = frc::MakeCovMatrix(visionMeasurmentStdDevs);
const wpi::array<double, 3>& visionMeasurementStdDevs) {
wpi::array<double, 3> r{wpi::empty_array};
for (size_t i = 0; i < 3; ++i) {
r[i] = visionMeasurementStdDevs[i] * visionMeasurementStdDevs[i];
}
// Solve for closed form Kalman gain for continuous Kalman filter with A = 0
// and C = I. See wpimath/algorithms.md.
for (size_t row = 0; row < 3; ++row) {
if (m_q[row] == 0.0) {
m_visionK(row, row) = 0.0;
} else {
m_visionK(row, row) =
m_q[row] / (m_q[row] + std::sqrt(m_q[row] * r[row]));
}
}
}
void frc::MecanumDrivePoseEstimator::ResetPosition(
const Rotation2d& gyroAngle,
const MecanumDriveWheelPositions& wheelPositions, const Pose2d& pose) {
// Reset state estimate and error covariance
m_observer.Reset();
m_odometry.ResetPosition(gyroAngle, wheelPositions, pose);
m_poseBuffer.Clear();
auto poseVec = PoseTo3dVector(pose);
auto xhat = Vectord<7>{
poseVec(0),
poseVec(1),
poseVec(2),
wheelPositions.frontLeft.value(),
wheelPositions.frontRight.value(),
wheelPositions.rearLeft.value(),
wheelPositions.rearRight.value(),
};
m_observer.SetXhat(xhat);
m_prevTime = -1_s;
m_gyroOffset = pose.Rotation() - gyroAngle;
m_previousAngle = pose.Rotation();
}
Pose2d frc::MecanumDrivePoseEstimator::GetEstimatedPosition() const {
return Pose2d{m_observer.Xhat(0) * 1_m, m_observer.Xhat(1) * 1_m,
units::radian_t{m_observer.Xhat(2)}};
return m_odometry.GetPose();
}
void frc::MecanumDrivePoseEstimator::AddVisionMeasurement(
const Pose2d& visionRobotPose, units::second_t timestamp) {
if (auto sample = m_poseBuffer.Sample(timestamp)) {
m_visionCorrect(Vectord<7>::Zero(),
PoseTo3dVector(GetEstimatedPosition().TransformBy(
visionRobotPose - sample.value())));
// Step 1: Get the estimated pose from when the vision measurement was made.
auto sample = m_poseBuffer.Sample(timestamp);
if (!sample.has_value()) {
return;
}
// Step 2: Measure the twist between the odometry pose and the vision pose
auto twist = sample.value().pose.Log(visionRobotPose);
// Step 3: We should not trust the twist entirely, so instead we scale this
// twist by a Kalman gain matrix representing how much we trust vision
// measurements compared to our current pose.
frc::Vectord<3> k_times_twist =
m_visionK *
frc::Vectord<3>{twist.dx.value(), twist.dy.value(), twist.dtheta.value()};
// Step 4: Convert back to Twist2d
Twist2d scaledTwist{units::meter_t{k_times_twist(0)},
units::meter_t{k_times_twist(1)},
units::radian_t{k_times_twist(2)}};
// Step 5: Reset Odometry to state at sample with vision adjustment.
m_odometry.ResetPosition(sample.value().gyroAngle,
sample.value().wheelPositions,
sample.value().pose.Exp(scaledTwist));
// Step 6: Replay odometry inputs between sample time and latest recorded
// sample to update the pose buffer and correct odometry.
auto internal_buf = m_poseBuffer.GetInternalBuffer();
auto upper_bound =
std::lower_bound(internal_buf.begin(), internal_buf.end(), timestamp,
[](const auto& pair, auto t) { return t > pair.first; });
for (auto entry = upper_bound; entry != internal_buf.end(); entry++) {
UpdateWithTime(entry->first, entry->second.gyroAngle,
entry->second.wheelPositions);
}
}
Pose2d frc::MecanumDrivePoseEstimator::Update(
const Rotation2d& gyroAngle, const MecanumDriveWheelSpeeds& wheelSpeeds,
const Rotation2d& gyroAngle,
const MecanumDriveWheelPositions& wheelPositions) {
return UpdateWithTime(units::microsecond_t(wpi::Now()), gyroAngle,
wheelSpeeds, wheelPositions);
wheelPositions);
}
Pose2d frc::MecanumDrivePoseEstimator::UpdateWithTime(
units::second_t currentTime, const Rotation2d& gyroAngle,
const MecanumDriveWheelSpeeds& wheelSpeeds,
const MecanumDriveWheelPositions& wheelPositions) {
auto dt = m_prevTime >= 0_s ? currentTime - m_prevTime : m_nominalDt;
m_prevTime = currentTime;
m_odometry.Update(gyroAngle, wheelPositions);
auto angle = gyroAngle + m_gyroOffset;
auto omega = (angle - m_previousAngle).Radians() / dt;
MecanumDriveWheelPositions internalWheelPositions{
wheelPositions.frontLeft, wheelPositions.frontRight,
wheelPositions.rearLeft, wheelPositions.rearRight};
auto chassisSpeeds = m_kinematics.ToChassisSpeeds(wheelSpeeds);
auto fieldRelativeVelocities =
Translation2d{chassisSpeeds.vx * 1_s, chassisSpeeds.vy * 1_s}.RotateBy(
angle);
Vectord<7> u{fieldRelativeVelocities.X().value(),
fieldRelativeVelocities.Y().value(),
omega.value(),
wheelSpeeds.frontLeft.value(),
wheelSpeeds.frontRight.value(),
wheelSpeeds.rearLeft.value(),
wheelSpeeds.rearRight.value()};
Vectord<5> localY{angle.Radians().value(), wheelPositions.frontLeft.value(),
wheelPositions.frontRight.value(),
wheelPositions.rearLeft.value(),
wheelPositions.rearRight.value()};
m_previousAngle = angle;
m_poseBuffer.AddSample(currentTime, GetEstimatedPosition());
m_observer.Predict(u, dt);
m_observer.Correct(u, localY);
m_poseBuffer.AddSample(
currentTime, {GetEstimatedPosition(), gyroAngle, internalWheelPositions});
return GetEstimatedPosition();
}

124
wpimath/src/main/native/include/frc/estimator/DifferentialDrivePoseEstimator.h
View File

@@ -12,12 +12,14 @@
#include "frc/geometry/Pose2d.h"
#include "frc/geometry/Rotation2d.h"
#include "frc/interpolation/TimeInterpolatableBuffer.h"
#include "frc/kinematics/DifferentialDriveKinematics.h"
#include "frc/kinematics/DifferentialDriveOdometry.h"
#include "frc/kinematics/DifferentialDriveWheelSpeeds.h"
#include "units/time.h"
namespace frc {
/**
* This class wraps an Unscented Kalman Filter to fuse latency-compensated
* This class wraps Differential Drive Odometry to fuse latency-compensated
* vision measurements with differential drive encoder measurements. It will
* correct for noisy vision measurements and encoder drift. It is intended to be
* an easy drop-in for DifferentialDriveOdometry. In fact, if you never call
@@ -31,30 +33,22 @@ namespace frc {
* AddVisionMeasurement() can be called as infrequently as you want; if you
* never call it, then this class will behave like regular encoder odometry.
*
* The state-space system used internally has the following states (x), inputs
* (u), and outputs (y):
* The state-space system used internally has the following states (x) and
* outputs (y):
*
* <strong> x = [x, y, theta, dist_l, dist_r]ᵀ </strong> in the field coordinate
* system containing x position, y position, heading, left encoder distance,
* and right encoder distance.
*
* <strong> u = [v_x, v_y, omega]ᵀ </strong> containing x velocity, y velocity,
* and angular velocity in the field coordinate system.
*
* NB: Using velocities make things considerably easier, because it means that
* teams don't have to worry about getting an accurate model. Basically, we
* suspect that it's easier for teams to get good encoder data than it is for
* them to perform system identification well enough to get a good model.
* <strong> x = [x, y, theta]ᵀ </strong> in the field coordinate
* system containing x position, y position, and heading.
*
* <strong> y = [x, y, theta]ᵀ </strong> from vision containing x position, y
* position, and heading; or <strong>y = [dist_l, dist_r, theta] </strong>
* containing left encoder position, right encoder position, and gyro heading.
* position, and heading.
*/
class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
public:
/**
* Constructs a DifferentialDrivePoseEstimator.
*
* @param kinematics A correctly-configured kinematics object
* for your drivetrain.
* @param gyroAngle The gyro angle of the robot.
* @param leftDistance The distance traveled by the left encoder.
* @param rightDistance The distance traveled by the right encoder.
@@ -65,28 +59,18 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
* is in the form
* [x, y, theta, dist_l, dist_r]ᵀ,
* with units in meters and radians.
* @param localMeasurementStdDevs Standard deviations of the encoder and gyro
* measurements. Increase these numbers to
* trust sensor readings from
* encoders and gyros less.
* This matrix is in the form
* [dist_l, dist_r, theta]ᵀ, with units in
* meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision
* measurements. Increase these numbers to
* trust global measurements from
* vision less. This matrix is in the form
* [x, y, theta]ᵀ, with units in meters and
* radians.
* @param nominalDt The period of the loop calling Update().
*/
DifferentialDrivePoseEstimator(
const Rotation2d& gyroAngle, units::meter_t leftDistance,
units::meter_t rightDistance, const Pose2d& initialPose,
const wpi::array<double, 5>& stateStdDevs,
const wpi::array<double, 3>& localMeasurementStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs,
units::second_t nominalDt = 20_ms);
DifferentialDriveKinematics& kinematics, const Rotation2d& gyroAngle,
units::meter_t leftDistance, units::meter_t rightDistance,
const Pose2d& initialPose, const wpi::array<double, 3>& stateStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs);
/**
* Sets the pose estimator's trust of global measurements. This might be used
@@ -106,11 +90,6 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
/**
* Resets the robot's position on the field.
*
* IF leftDistance and rightDistance are unspecified,
* You NEED to reset your encoders (to zero). The
* gyroscope angle does not need to be reset here on the user's robot code.
* The library automatically takes care of offsetting the gyro angle.
*
* @param gyroAngle The current gyro angle.
* @param leftDistance The distance traveled by the left encoder.
* @param rightDistance The distance traveled by the right encoder.
@@ -120,15 +99,14 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
units::meter_t rightDistance, const Pose2d& pose);
/**
* Returns the pose of the robot at the current time as estimated by the
* Unscented Kalman Filter.
* Gets the estimated robot pose.
*
* @return The estimated robot pose.
*/
Pose2d GetEstimatedPosition() const;
/**
* Adds a vision measurement to the Unscented Kalman Filter. This will correct
* Adds a vision measurement to the Kalman Filter. This will correct
* the odometry pose estimate while still accounting for measurement noise.
*
* This method can be called as infrequently as you want, as long as you are
@@ -153,7 +131,7 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
units::second_t timestamp);
/**
* Adds a vision measurement to the Unscented Kalman Filter. This will correct
* Adds a vision measurement to the Kalman Filter. This will correct
* the odometry pose estimate while still accounting for measurement noise.
*
* This method can be called as infrequently as you want, as long as you are
@@ -199,15 +177,13 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
* Note that this should be called every loop iteration.
*
* @param gyroAngle The current gyro angle.
* @param wheelSpeeds The velocities of the wheels in meters per second.
* @param leftDistance The distance traveled by the left encoder.
* @param rightDistance The distance traveled by the right encoder.
*
* @return The estimated pose of the robot.
*/
Pose2d Update(const Rotation2d& gyroAngle,
const DifferentialDriveWheelSpeeds& wheelSpeeds,
units::meter_t leftDistance, units::meter_t rightDistance);
Pose2d Update(const Rotation2d& gyroAngle, units::meter_t leftDistance,
units::meter_t rightDistance);
/**
* Updates the Unscented Kalman Filter using only wheel encoder information.
@@ -215,7 +191,6 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
*
* @param currentTime The time at which this method was called.
* @param gyroAngle The current gyro angle.
* @param wheelSpeeds The velocities of the wheels in meters per second.
* @param leftDistance The distance traveled by the left encoder.
* @param rightDistance The distance traveled by the right encoder.
*
@@ -223,27 +198,62 @@ class WPILIB_DLLEXPORT DifferentialDrivePoseEstimator {
*/
Pose2d UpdateWithTime(units::second_t currentTime,
const Rotation2d& gyroAngle,
const DifferentialDriveWheelSpeeds& wheelSpeeds,
units::meter_t leftDistance,
units::meter_t rightDistance);
private:
UnscentedKalmanFilter<5, 3, 3> m_observer;
TimeInterpolatableBuffer<Pose2d> m_poseBuffer{1.5_s};
std::function<void(const Vectord<3>& u, const Vectord<3>& y)> m_visionCorrect;
struct InterpolationRecord {
// The pose observed given the current sensor inputs and the previous pose.
Pose2d pose;
Matrixd<3, 3> m_visionContR;
// The current gyro angle.
Rotation2d gyroAngle;
units::second_t m_nominalDt;
units::second_t m_prevTime = -1_s;
// The distance traveled by the left encoder.
units::meter_t leftDistance;
Rotation2d m_gyroOffset;
Rotation2d m_previousAngle;
// The distance traveled by the right encoder.
units::meter_t rightDistance;
static Vectord<5> F(const Vectord<5>& x, const Vectord<3>& u);
static Vectord<5> FillStateVector(const Pose2d& pose,
units::meter_t leftDistance,
units::meter_t rightDistance);
/**
* Checks equality between this InterpolationRecord and another object.
*
* @param other The other object.
* @return Whether the two objects are equal.
*/
bool operator==(const InterpolationRecord& other) const = default;
/**
* Checks inequality between this InterpolationRecord and another object.
*
* @param other The other object.
* @return Whether the two objects are not equal.
*/
bool operator!=(const InterpolationRecord& other) const = default;
/**
* Interpolates between two InterpolationRecords.
*
* @param endValue The end value for the interpolation.
* @param i The interpolant (fraction).
*
* @return The interpolated state.
*/
InterpolationRecord Interpolate(DifferentialDriveKinematics& kinematics,
InterpolationRecord endValue,
double i) const;
};
DifferentialDriveKinematics& m_kinematics;
DifferentialDriveOdometry m_odometry;
wpi::array<double, 3> m_q{wpi::empty_array};
Eigen::Matrix3d m_visionK = Eigen::Matrix3d::Zero();
TimeInterpolatableBuffer<InterpolationRecord> m_poseBuffer{
1.5_s, [this](const InterpolationRecord& start,
const InterpolationRecord& end, double t) {
return start.Interpolate(this->m_kinematics, end, t);
}};
};
} // namespace frc

130
wpimath/src/main/native/include/frc/estimator/MecanumDrivePoseEstimator.h
View File

@@ -15,14 +15,15 @@
#include "frc/geometry/Rotation2d.h"
#include "frc/interpolation/TimeInterpolatableBuffer.h"
#include "frc/kinematics/MecanumDriveKinematics.h"
#include "frc/kinematics/MecanumDriveOdometry.h"
#include "units/time.h"
namespace frc {
/**
* This class wraps an Unscented Kalman Filter to fuse latency-compensated
* This class wraps Mecanum Drive Odometry to fuse latency-compensated
* vision measurements with mecanum drive encoder velocity measurements. It will
* correct for noisy measurements and encoder drift. It is intended to be an
* easy but more accurate drop-in for MecanumDriveOdometry.
* easy drop-in for MecanumDriveOdometry.
*
* Update() should be called every robot loop. If your loops are faster or
* slower than the default of 20 ms, then you should change the nominal delta
@@ -32,63 +33,43 @@ namespace frc {
* never call it, then this class will behave mostly like regular encoder
* odometry.
*
* The state-space system used internally has the following states (x), inputs
* (u), and outputs (y):
* The state-space system used internally has the following states (x) and
* outputs (y):
*
* <strong> x = [x, y, theta, s_fl, s_fr, s_rl, s_rr]ᵀ </strong> in the field
* coordinate system containing x position, y position, and heading, followed
* by the distance driven by the front left, front right, rear left, and rear
* right wheels.
*
* <strong> u = [v_x, v_y, omega, v_fl, v_fr, v_rl, v_rr]ᵀ </strong> containing
* x velocity, y velocity, and angular rate in the field coordinate system,
* followed by the velocity of the front left, front right, rear left, and rear
* right wheels.
* <strong> x = [x, y, theta]ᵀ </strong> in the field
* coordinate system containing x position, y position, and heading.
*
* <strong> y = [x, y, theta]ᵀ </strong> from vision containing x position, y
* position, and heading; or <strong> y = [theta, s_fl, s_fr, s_rl, s_rr]ᵀ
* </strong> containing gyro heading, followed by the distance driven by the
* front left, front right, rear left, and rear right wheels.
* position, and heading.
*/
class WPILIB_DLLEXPORT MecanumDrivePoseEstimator {
public:
/**
* Constructs a MecanumDrivePoseEstimator.
*
* @param kinematics A correctly-configured kinematics object
* for your drivetrain.
* @param gyroAngle The current gyro angle.
* @param wheelPositions The distance measured by each wheel.
* @param initialPose The starting pose estimate.
* @param kinematics A correctly-configured kinematics object
* for your drivetrain.
* @param stateStdDevs Standard deviations of model states.
* Increase these numbers to trust your
* model's state estimates less. This matrix
* is in the form [x, y, theta, s_fl, s_fr,
* s_rl, s_rr]ᵀ, with units in meters and
* radians, followed by meters.
* @param localMeasurementStdDevs Standard deviation of the gyro
* measurement. Increase this number to trust
* sensor readings from the gyro less. This
* matrix is in the form [theta, s_fl, s_fr,
* s_rl, s_rr], with units in radians,
* followed by meters.
* is in the form [x, y, theta]ᵀ, with units
* in meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision
* measurements. Increase these numbers to
* trust global measurements from vision
* less. This matrix is in the form
* [x, y, theta]ᵀ, with units in meters and
* radians.
* @param nominalDt The time in seconds between each robot
* loop.
*/
MecanumDrivePoseEstimator(
const Rotation2d& gyroAngle,
MecanumDriveKinematics& kinematics, const Rotation2d& gyroAngle,
const MecanumDriveWheelPositions& wheelPositions,
const Pose2d& initialPose, MecanumDriveKinematics kinematics,
const wpi::array<double, 7>& stateStdDevs,
const wpi::array<double, 5>& localMeasurementStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs,
units::second_t nominalDt = 20_ms);
const Pose2d& initialPose, const wpi::array<double, 3>& stateStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs);
/**
* Sets the pose estimator's trust of global measurements. This might be used
@@ -108,9 +89,6 @@ class WPILIB_DLLEXPORT MecanumDrivePoseEstimator {
/**
* Resets the robot's position on the field.
*
* IF wheelPositions are unspecified,
* You NEED to reset your encoders (to zero).
*
* The gyroscope angle does not need to be reset in the user's robot code.
* The library automatically takes care of offsetting the gyro angle.
*
@@ -123,15 +101,14 @@ class WPILIB_DLLEXPORT MecanumDrivePoseEstimator {
const Pose2d& pose);
/**
* Gets the pose of the robot at the current time as estimated by the Extended
* Kalman Filter.
* Gets the estimated robot pose.
*
* @return The estimated robot pose in meters.
*/
Pose2d GetEstimatedPosition() const;
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct
* Add a vision measurement to the Kalman Filter. This will correct
* the odometry pose estimate while still accounting for measurement noise.
*
* This method can be called as infrequently as you want, as long as you are
@@ -156,7 +133,7 @@ class WPILIB_DLLEXPORT MecanumDrivePoseEstimator {
units::second_t timestamp);
/**
* Adds a vision measurement to the Unscented Kalman Filter. This will correct
* Adds a vision measurement to the Kalman Filter. This will correct
* the odometry pose estimate while still accounting for measurement noise.
*
* This method can be called as infrequently as you want, as long as you are
@@ -198,48 +175,79 @@ class WPILIB_DLLEXPORT MecanumDrivePoseEstimator {
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder
* information. This should be called every loop, and the correct loop period
* must be passed into the constructor of this class.
* Updates the the Kalman Filter using only wheel encoder
* information. This should be called every loop.
*
* @param gyroAngle The current gyro angle.
* @param wheelSpeeds The current speeds of the mecanum drive wheels.
* @param wheelPositions The distances measured at each wheel.
* @return The estimated pose of the robot in meters.
*/
Pose2d Update(const Rotation2d& gyroAngle,
const MecanumDriveWheelSpeeds& wheelSpeeds,
const MecanumDriveWheelPositions& wheelPositions);
/**
* Updates the the Unscented Kalman Filter using only wheel encoder
* information. This should be called every loop, and the correct loop period
* must be passed into the constructor of this class.
* Updates the the Kalman Filter using only wheel encoder
* information. This should be called every loop.
*
* @param currentTime Time at which this method was called, in seconds.
* @param gyroAngle The current gyroscope angle.
* @param wheelSpeeds The current speeds of the mecanum drive wheels.
* @param wheelPositions The distances measured at each wheel.
* @return The estimated pose of the robot in meters.
*/
Pose2d UpdateWithTime(units::second_t currentTime,
const Rotation2d& gyroAngle,
const MecanumDriveWheelSpeeds& wheelSpeeds,
const MecanumDriveWheelPositions& wheelPositions);
private:
UnscentedKalmanFilter<7, 7, 5> m_observer;
MecanumDriveKinematics m_kinematics;
TimeInterpolatableBuffer<Pose2d> m_poseBuffer{1.5_s};
std::function<void(const Vectord<7>& u, const Vectord<3>& y)> m_visionCorrect;
struct InterpolationRecord {
// The pose observed given the current sensor inputs and the previous pose.
Pose2d pose;
Eigen::Matrix3d m_visionContR;
// The current gyroscope angle.
Rotation2d gyroAngle;
units::second_t m_nominalDt;
units::second_t m_prevTime = -1_s;
// The distances measured at each wheel.
MecanumDriveWheelPositions wheelPositions;
Rotation2d m_gyroOffset;
Rotation2d m_previousAngle;
/**
* Checks equality between this InterpolationRecord and another object.
*
* @param other The other object.
* @return Whether the two objects are equal.
*/
bool operator==(const InterpolationRecord& other) const = default;
/**
* Checks inequality between this InterpolationRecord and another object.
*
* @param other The other object.
* @return Whether the two objects are not equal.
*/
bool operator!=(const InterpolationRecord& other) const = default;
/**
* Interpolates between two InterpolationRecords.
*
* @param endValue The end value for the interpolation.
* @param i The interpolant (fraction).
*
* @return The interpolated state.
*/
InterpolationRecord Interpolate(MecanumDriveKinematics& kinematics,
InterpolationRecord endValue,
double i) const;
};
MecanumDriveKinematics& m_kinematics;
MecanumDriveOdometry m_odometry;
wpi::array<double, 3> m_q{wpi::empty_array};
Eigen::Matrix3d m_visionK = Eigen::Matrix3d::Zero();
TimeInterpolatableBuffer<InterpolationRecord> m_poseBuffer{
1.5_s, [this](const InterpolationRecord& start,
const InterpolationRecord& end, double t) {
return start.Interpolate(this->m_kinematics, end, t);
}};
};
} // namespace frc

354
wpimath/src/main/native/include/frc/estimator/SwerveDrivePoseEstimator.h
View File

@@ -4,143 +4,85 @@
#pragma once
#include <limits>
#include <cmath>
#include <fmt/format.h>
#include <wpi/SymbolExports.h>
#include <wpi/array.h>
#include <wpi/timestamp.h>
#include "frc/EigenCore.h"
#include "frc/StateSpaceUtil.h"
#include "frc/estimator/AngleStatistics.h"
#include "frc/estimator/UnscentedKalmanFilter.h"
#include "frc/geometry/Pose2d.h"
#include "frc/geometry/Rotation2d.h"
#include "frc/interpolation/TimeInterpolatableBuffer.h"
#include "frc/kinematics/SwerveDriveKinematics.h"
#include "frc/kinematics/SwerveDriveOdometry.h"
#include "units/time.h"
namespace frc {
/**
* This class wraps an Unscented Kalman Filter to fuse latency-compensated
* vision measurements with swerve drive encoder velocity measurements. It will
* correct for noisy measurements and encoder drift. It is intended to be an
* easy but more accurate drop-in for SwerveDriveOdometry.
* This class wraps Swerve Drive Odometry to fuse latency-compensated
* vision measurements with swerve drive encoder distance measurements. It is
* intended to be a drop-in for SwerveDriveOdometry.
*
* Update() should be called every robot loop. If your loops are faster or
* slower than the default of 20 ms, then you should change the nominal delta
* time by specifying it in the constructor.
* Update() should be called every robot loop.
*
* AddVisionMeasurement() can be called as infrequently as you want; if you
* never call it, then this class will behave mostly like regular encoder
* never call it, then this class will behave as regular encoder
* odometry.
*
* The state-space system used internally has the following states (x), inputs
* (u), and outputs (y):
* The state-space system used internally has the following states (x) and
* outputs (y):
*
* <strong> x = [x, y, theta, s_0, ..., s_n]ᵀ </strong> in the field coordinate
* system containing x position, y position, and heading, followed by the
* distance travelled by each wheel.
*
* <strong> u = [v_x, v_y, omega, v_0, ... v_n]ᵀ </strong> containing x
* velocity, y velocity, and angular velocity in the field coordinate system,
* followed by the velocity measured at each wheel.
* <strong> x = [x, y, theta]ᵀ </strong> in the field coordinate
* system containing x position, y position, and heading.
*
* <strong> y = [x, y, theta]ᵀ </strong> from vision containing x position, y
* position, and heading; or <strong> y = [theta, s_0, ..., s_n]ᵀ </strong>
* containing gyro heading, followed by the distance travelled by each wheel.
* position, and heading.
*/
template <size_t NumModules>
class SwerveDrivePoseEstimator {
public:
static constexpr size_t States = 3 + NumModules;
static constexpr size_t Inputs = 3 + NumModules;
static constexpr size_t Outputs = 1 + NumModules;
/**
* Constructs a SwerveDrivePoseEstimator.
*
* @param kinematics A correctly-configured kinematics object
* for your drivetrain.
* @param gyroAngle The current gyro angle.
* @param modulePositions The current distance and rotation
* measurements of the swerve modules.
* @param initialPose The starting pose estimate.
* @param kinematics A correctly-configured kinematics object
* for your drivetrain.
* @param stateStdDevs Standard deviations of model states.
* Increase these numbers to trust your
* model's state estimates less. This matrix
* is in the form [x, y, theta, s_0, ...
* s_n]ᵀ, with units in meters and radians, then meters.
* @param localMeasurementStdDevs Standard deviation of the gyro measurement.
* Increase this number to trust sensor
* readings from the gyro less. This matrix is
* in the form [theta, s_0, ... s_n], with
* units in radians followed by meters.
* is in the form [x, y, theta]ᵀ, with units
* in meters and radians.
* @param visionMeasurementStdDevs Standard deviations of the vision
* measurements. Increase these numbers to
* trust global measurements from vision
* less. This matrix is in the form
* [x, y, theta]ᵀ, with units in meters and
* radians.
* @param nominalDt The time in seconds between each robot
* loop.
*/
SwerveDrivePoseEstimator(
SwerveDriveKinematics<NumModules>& kinematics,
const Rotation2d& gyroAngle,
const wpi::array<SwerveModulePosition, NumModules>& modulePositions,
const Pose2d& initialPose, SwerveDriveKinematics<NumModules>& kinematics,
const wpi::array<double, States>& stateStdDevs,
const wpi::array<double, Outputs>& localMeasurementStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs,
units::second_t nominalDt = 20_ms)
: m_observer([](const Vectord<States>& x,
const Vectord<Inputs>& u) { return u; },
[](const Vectord<States>& x, const Vectord<Inputs>& u) {
return x.template block<States - 2, 1>(2, 0);
},
stateStdDevs, localMeasurementStdDevs,
frc::AngleMean<States, States>(2),
frc::AngleMean<Outputs, States>(0),
frc::AngleResidual<States>(2),
frc::AngleResidual<Outputs>(0), frc::AngleAdd<States>(2),
nominalDt),
m_kinematics(kinematics),
m_nominalDt(nominalDt) {
SetVisionMeasurementStdDevs(visionMeasurementStdDevs);
// Create correction mechanism for vision measurements.
m_visionCorrect = [&](const Vectord<Inputs>& u, const Vectord<3>& y) {
m_observer.template Correct<3>(
u, y,
[](const Vectord<States>& x, const Vectord<Inputs>& u) {
return x.template block<3, 1>(0, 0);
},
m_visionContR, frc::AngleMean<3, States>(2), frc::AngleResidual<3>(2),
frc::AngleResidual<States>(2), frc::AngleAdd<States>(2));
};
// Set initial state.
Vectord<States> xhat;
auto poseVec = PoseTo3dVector(initialPose);
xhat(0) = poseVec(0);
xhat(1) = poseVec(1);
xhat(2) = poseVec(2);
for (size_t i = 0; i < NumModules; i++) {
xhat(3 + i) = modulePositions[i].distance.value();
const Pose2d& initialPose, const wpi::array<double, 3>& stateStdDevs,
const wpi::array<double, 3>& visionMeasurementStdDevs)
: m_kinematics{kinematics},
m_odometry{kinematics, gyroAngle, modulePositions, initialPose} {
for (size_t i = 0; i < 3; ++i) {
m_q[i] = stateStdDevs[i] * stateStdDevs[i];
}
m_observer.SetXhat(xhat);
// Calculate offsets.
m_gyroOffset = initialPose.Rotation() - gyroAngle;
m_previousAngle = initialPose.Rotation();
SetVisionMeasurementStdDevs(visionMeasurementStdDevs);
}
/**
* Resets the robot's position on the field.
*
* IF leftDistance and rightDistance are unspecified,
* You NEED to reset your encoders (to zero).
*
* The gyroscope angle does not need to be reset in the user's robot code.
* The library automatically takes care of offsetting the gyro angle.
*
@@ -154,35 +96,16 @@ class SwerveDrivePoseEstimator {
const wpi::array<SwerveModulePosition, NumModules>& modulePositions,
const Pose2d& pose) {
// Reset state estimate and error covariance
m_observer.Reset();
m_odometry.ResetPosition(gyroAngle, modulePositions, pose);
m_poseBuffer.Clear();
Vectord<States> xhat;
auto poseVec = PoseTo3dVector(pose);
xhat(0) = poseVec(0);
xhat(1) = poseVec(1);
xhat(2) = poseVec(2);
for (size_t i = 0; i < NumModules; i++) {
xhat(3 + i) = modulePositions[i].distance.value();
}
m_observer.SetXhat(xhat);
m_prevTime = -1_s;
m_gyroOffset = pose.Rotation() - gyroAngle;
m_previousAngle = pose.Rotation();
}
/**
* Gets the pose of the robot at the current time as estimated by the Extended
* Kalman Filter.
* Gets the estimated robot pose.
*
* @return The estimated robot pose in meters.
*/
Pose2d GetEstimatedPosition() const {
return Pose2d{m_observer.Xhat(0) * 1_m, m_observer.Xhat(1) * 1_m,
Rotation2d{units::radian_t{m_observer.Xhat(2)}}};
}
Pose2d GetEstimatedPosition() const { return m_odometry.GetPose(); }
/**
* Sets the pose estimator's trust of global measurements. This might be used
@@ -198,13 +121,26 @@ class SwerveDrivePoseEstimator {
*/
void SetVisionMeasurementStdDevs(
const wpi::array<double, 3>& visionMeasurementStdDevs) {
// Create R (covariances) for vision measurements.
m_visionContR = frc::MakeCovMatrix(visionMeasurementStdDevs);
wpi::array<double, 3> r{wpi::empty_array};
for (size_t i = 0; i < 3; ++i) {
r[i] = visionMeasurementStdDevs[i] * visionMeasurementStdDevs[i];
}
// Solve for closed form Kalman gain for continuous Kalman filter with A = 0
// and C = I. See wpimath/algorithms.md.
for (size_t row = 0; row < 3; ++row) {
if (m_q[row] == 0.0) {
m_visionK(row, row) = 0.0;
} else {
m_visionK(row, row) =
m_q[row] / (m_q[row] + std::sqrt(m_q[row] * r[row]));
}
}
}
/**
* Add a vision measurement to the Unscented Kalman Filter. This will correct
* the odometry pose estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the
* odometry pose estimate while still accounting for measurement noise.
*
* This method can be called as infrequently as you want, as long as you are
* calling Update() every loop.
@@ -226,16 +162,50 @@ class SwerveDrivePoseEstimator {
*/
void AddVisionMeasurement(const Pose2d& visionRobotPose,
units::second_t timestamp) {
if (auto sample = m_poseBuffer.Sample(timestamp)) {
m_visionCorrect(Vectord<States>::Zero(),
PoseTo3dVector(GetEstimatedPosition().TransformBy(
visionRobotPose - sample.value())));
// Step 1: Get the estimated pose from when the vision measurement was made.
auto sample = m_poseBuffer.Sample(timestamp);
if (!sample.has_value()) {
return;
}
// Step 2: Measure the twist between the odometry pose and the vision pose
auto twist = sample.value().pose.Log(visionRobotPose);
// Step 3: We should not trust the twist entirely, so instead we scale this
// twist by a Kalman gain matrix representing how much we trust vision
// measurements compared to our current pose.
frc::Vectord<3> k_times_twist =
m_visionK * frc::Vectord<3>{twist.dx.value(), twist.dy.value(),
twist.dtheta.value()};
// Step 4: Convert back to Twist2d
Twist2d scaledTwist{units::meter_t{k_times_twist(0)},
units::meter_t{k_times_twist(1)},
units::radian_t{k_times_twist(2)}};
// Step 5: Reset Odometry to state at sample with vision adjustment.
m_odometry.ResetPosition(sample.value().gyroAngle,
sample.value().modulePostions,
sample.value().pose.Exp(scaledTwist));
// Step 6: Replay odometry inputs between sample time and latest recorded
// sample to update the pose buffer and correct odometry.
auto internal_buf = m_poseBuffer.GetInternalBuffer();
auto upper_bound = std::lower_bound(
internal_buf.begin(), internal_buf.end(), timestamp,
[](const auto& pair, auto t) { return t > pair.first; });
for (auto entry = upper_bound; entry != internal_buf.end(); entry++) {
UpdateWithTime(entry->first, entry->second.gyroAngle,
entry->second.modulePostions);
}
}
/**
* Adds a vision measurement to the Unscented Kalman Filter. This will correct
* the odometry pose estimate while still accounting for measurement noise.
* Adds a vision measurement to the Kalman Filter. This will correct the
* odometry pose estimate while still accounting for measurement noise.
*
* This method can be called as infrequently as you want, as long as you are
* calling Update() every loop.
@@ -276,91 +246,137 @@ class SwerveDrivePoseEstimator {
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder
* information. This should be called every loop, and the correct loop period
* must be passed into the constructor of this class.
* Updates the Kalman Filter using only wheel encoder information. This should
* be called every loop.
*
* @param gyroAngle The current gyro angle.
* @param moduleStates The current velocities and rotations of the swerve
* modules.
* @param modulePositions The current distance and rotation measurements of
* the swerve modules.
* @return The estimated pose of the robot in meters.
* @return The estimated robot pose in meters.
*/
Pose2d Update(
const Rotation2d& gyroAngle,
const wpi::array<SwerveModuleState, NumModules>& moduleStates,
const wpi::array<SwerveModulePosition, NumModules>& modulePositions) {
return UpdateWithTime(units::microsecond_t(wpi::Now()), gyroAngle,
moduleStates, modulePositions);
modulePositions);
}
/**
* Updates the the Unscented Kalman Filter using only wheel encoder
* information. This should be called every loop, and the correct loop period
* must be passed into the constructor of this class.
* Updates the Kalman Filter using only wheel encoder information. This should
* be called every loop.
*
* @param currentTime Time at which this method was called, in seconds.
* @param gyroAngle The current gyro angle.
* @param moduleStates The current velocities and rotations of the swerve
* modules.
* @param modulePositions The current distance travelled and rotations of
* @param modulePositions The current distance traveled and rotations of
* the swerve modules.
* @return The estimated pose of the robot in meters.
* @return The estimated robot pose in meters.
*/
Pose2d UpdateWithTime(
units::second_t currentTime, const Rotation2d& gyroAngle,
const wpi::array<SwerveModuleState, NumModules>& moduleStates,
const wpi::array<SwerveModulePosition, NumModules>& modulePositions) {
auto dt = m_prevTime >= 0_s ? currentTime - m_prevTime : m_nominalDt;
m_prevTime = currentTime;
m_odometry.Update(gyroAngle, modulePositions);
auto angle = gyroAngle + m_gyroOffset;
auto omega = (angle - m_previousAngle).Radians() / dt;
wpi::array<SwerveModulePosition, NumModules> internalModulePositions{
wpi::empty_array};
auto chassisSpeeds = m_kinematics.ToChassisSpeeds(moduleStates);
auto fieldRelativeSpeeds =
Translation2d{chassisSpeeds.vx * 1_s, chassisSpeeds.vy * 1_s}.RotateBy(
angle);
Vectord<Inputs> u;
u(0) = fieldRelativeSpeeds.X().value();
u(1) = fieldRelativeSpeeds.Y().value();
u(2) = omega.value();
for (size_t i = 0; i < NumModules; i++) {
u(3 + i) = moduleStates[i].speed.value();
internalModulePositions[i].distance = modulePositions[i].distance;
internalModulePositions[i].angle = modulePositions[i].angle;
}
Vectord<Outputs> localY;
localY(0) = angle.Radians().value();
for (size_t i = 0; i < NumModules; i++) {
localY(1 + i) = modulePositions[i].distance.value();
}
m_previousAngle = angle;
m_poseBuffer.AddSample(currentTime, GetEstimatedPosition());
m_observer.Predict(u, dt);
m_observer.Correct(u, localY);
m_poseBuffer.AddSample(currentTime, {GetEstimatedPosition(), gyroAngle,
internalModulePositions});
return GetEstimatedPosition();
}
private:
UnscentedKalmanFilter<States, Inputs, Outputs> m_observer;
struct InterpolationRecord {
// The pose observed given the current sensor inputs and the previous pose.
Pose2d pose;
// The current gyroscope angle.
Rotation2d gyroAngle;
// The distances traveled and rotations meaured at each module.
wpi::array<SwerveModulePosition, NumModules> modulePostions;
/**
* Checks equality between this InterpolationRecord and another object.
*
* @param other The other object.
* @return Whether the two objects are equal.
*/
bool operator==(const InterpolationRecord& other) const = default;
/**
* Checks inequality between this InterpolationRecord and another object.
*
* @param other The other object.
* @return Whether the two objects are not equal.
*/
bool operator!=(const InterpolationRecord& other) const = default;
/**
* Interpolates between two InterpolationRecords.
*
* @param endValue The end value for the interpolation.
* @param i The interpolant (fraction).
*
* @return The interpolated state.
*/
InterpolationRecord Interpolate(
SwerveDriveKinematics<NumModules>& kinematics,
InterpolationRecord endValue, double i) const {
if (i < 0) {
return *this;
} else if (i > 1) {
return endValue;
} else {
// Find the new module distances.
wpi::array<SwerveModulePosition, NumModules> modulePositions{
wpi::empty_array};
// Find the distance between this measurement and the
// interpolated measurement.
wpi::array<SwerveModulePosition, NumModules> modulesDelta{
wpi::empty_array};
for (size_t i = 0; i < NumModules; i++) {
modulePositions[i].distance =
wpi::Lerp(this->modulePostions[i].distance,
endValue.modulePostions[i].distance, i);
modulePositions[i].angle =
wpi::Lerp(this->modulePostions[i].angle,
endValue.modulePostions[i].angle, i);
modulesDelta[i].distance =
modulePositions[i].distance - this->modulePostions[i].distance;
modulesDelta[i].angle = modulePositions[i].angle;
}
// Find the new gyro angle.
auto gyro = wpi::Lerp(this->gyroAngle, endValue.gyroAngle, i);
// Create a twist to represent this changed based on the interpolated
// sensor inputs.
auto twist = kinematics.ToTwist2d(modulesDelta);
twist.dtheta = (gyro - gyroAngle).Radians();
return {pose.Exp(twist), gyro, modulePositions};
}
}
};
SwerveDriveKinematics<NumModules>& m_kinematics;
TimeInterpolatableBuffer<Pose2d> m_poseBuffer{1.5_s};
std::function<void(const Vectord<Inputs>& u, const Vectord<3>& y)>
m_visionCorrect;
SwerveDriveOdometry<NumModules> m_odometry;
wpi::array<double, 3> m_q{wpi::empty_array};
Eigen::Matrix3d m_visionK = Eigen::Matrix3d::Zero();
Eigen::Matrix3d m_visionContR;
units::second_t m_nominalDt;
units::second_t m_prevTime = -1_s;
Rotation2d m_gyroOffset;
Rotation2d m_previousAngle;
TimeInterpolatableBuffer<InterpolationRecord> m_poseBuffer{
1.5_s, [this](const InterpolationRecord& start,
const InterpolationRecord& end, double t) {
return start.Interpolate(this->m_kinematics, end, t);
}};
};
extern template class EXPORT_TEMPLATE_DECLARE(WPILIB_DLLEXPORT)

8
wpimath/src/main/native/include/frc/interpolation/TimeInterpolatableBuffer.h
View File

@@ -120,6 +120,14 @@ class TimeInterpolatableBuffer {
return m_interpolatingFunc(lower_bound->second, upper_bound->second, t);
}
/**
* Grant access to the internal sample buffer. Used in Pose Estimation to
* replay odometry inputs stored within this buffer.
*/
std::vector<std::pair<units::second_t, T>>& GetInternalBuffer() {
return m_pastSnapshots;
}
private:
units::second_t m_historySize;
std::vector<std::pair<units::second_t, T>> m_pastSnapshots;

15
wpimath/src/main/native/include/frc/kinematics/DifferentialDriveKinematics.h
View File

@@ -6,6 +6,7 @@
#include <wpi/SymbolExports.h>
#include "frc/geometry/Twist2d.h"
#include "frc/kinematics/ChassisSpeeds.h"
#include "frc/kinematics/DifferentialDriveWheelSpeeds.h"
#include "units/angle.h"
@@ -64,6 +65,20 @@ class WPILIB_DLLEXPORT DifferentialDriveKinematics {
chassisSpeeds.vx + trackWidth / 2 * chassisSpeeds.omega / 1_rad};
}
/**
* Returns a twist from left and right distance deltas using
* forward kinematics.
*
* @param leftDistance The distance measured by the left encoder.
* @param rightDistance The distance measured by the right encoder.
* @return The resulting Twist2d.
*/
constexpr Twist2d ToTwist2d(const units::meter_t leftDistance,
const units::meter_t rightDistance) const {
return {(leftDistance + rightDistance) / 2, 0_m,
(rightDistance - leftDistance) / trackWidth * 1_rad};
}
units::meter_t trackWidth;
};
} // namespace frc

4
wpimath/src/main/native/include/frc/kinematics/DifferentialDriveOdometry.h
View File

@@ -60,8 +60,8 @@ class WPILIB_DLLEXPORT DifferentialDriveOdometry {
m_previousAngle = pose.Rotation();
m_gyroOffset = m_pose.Rotation() - gyroAngle;
m_prevLeftDistance = 0_m;
m_prevRightDistance = 0_m;
m_prevLeftDistance = leftDistance;
m_prevRightDistance = rightDistance;
}
/**

17
wpimath/src/main/native/include/frc/kinematics/MecanumDriveWheelPositions.h
View File

@@ -32,5 +32,22 @@ struct WPILIB_DLLEXPORT MecanumDriveWheelPositions {
* Distance driven by the rear-right wheel.
*/
units::meter_t rearRight = 0_m;
/**
* Checks equality between this MecanumDriveWheelPositions and another object.
*
* @param other The other object.
* @return Whether the two objects are equal.
*/
bool operator==(const MecanumDriveWheelPositions& other) const = default;
/**
* Checks inequality between this MecanumDriveWheelPositions and another
* object.
*
* @param other The other object.
* @return Whether the two objects are not equal.
*/
bool operator!=(const MecanumDriveWheelPositions& other) const = default;
};
} // namespace frc

11
wpimath/src/main/native/include/frc/kinematics/SwerveDriveOdometry.h
View File

@@ -66,11 +66,9 @@ class SwerveDriveOdometry {
/**
* Updates the robot's position on the field using forward kinematics and
* integration of the pose over time. This method takes in the current time as
* a parameter to calculate period (difference between two timestamps). The
* period is used to calculate the change in distance from a velocity. This
* also takes in an angle parameter which is used instead of the
* angular rate that is calculated from forward kinematics.
* integration of the pose over time. This also takes in an angle parameter
* which is used instead of the angular rate that is calculated from forward
* kinematics.
*
* @param gyroAngle The angle reported by the gyroscope.
* @param modulePositions The current position of all swerve modules. Please
@@ -90,7 +88,8 @@ class SwerveDriveOdometry {
Rotation2d m_previousAngle;
Rotation2d m_gyroOffset;
wpi::array<SwerveModulePosition, NumModules> m_previousModulePositions;
wpi::array<SwerveModulePosition, NumModules> m_previousModulePositions{
wpi::empty_array};
};
extern template class EXPORT_TEMPLATE_DECLARE(WPILIB_DLLEXPORT)

20
wpimath/src/main/native/include/frc/kinematics/SwerveDriveOdometry.inc
View File

@@ -13,11 +13,15 @@ SwerveDriveOdometry<NumModules>::SwerveDriveOdometry(
SwerveDriveKinematics<NumModules> kinematics, const Rotation2d& gyroAngle,
const wpi::array<SwerveModulePosition, NumModules>& modulePositions,
const Pose2d& initialPose)
: m_kinematics(kinematics),
m_pose(initialPose),
m_previousModulePositions(modulePositions) {
: m_kinematics(kinematics), m_pose(initialPose) {
m_previousAngle = m_pose.Rotation();
m_gyroOffset = m_pose.Rotation() - gyroAngle;
for (size_t i = 0; i < NumModules; i++) {
m_previousModulePositions[i] = {modulePositions[i].distance,
modulePositions[i].angle};
}
wpi::math::MathSharedStore::ReportUsage(
wpi::math::MathUsageId::kOdometry_SwerveDrive, 1);
}
@@ -30,7 +34,10 @@ void SwerveDriveOdometry<NumModules>::ResetPosition(
m_pose = pose;
m_previousAngle = pose.Rotation();
m_gyroOffset = m_pose.Rotation() - gyroAngle;
m_previousModulePositions = modulePositions;
for (size_t i = 0; i < NumModules; i++) {
m_previousModulePositions[i].distance = modulePositions[i].distance;
}
}
template <size_t NumModules>
@@ -39,11 +46,13 @@ const Pose2d& frc::SwerveDriveOdometry<NumModules>::Update(
const wpi::array<SwerveModulePosition, NumModules>& modulePositions) {
auto moduleDeltas =
wpi::array<SwerveModulePosition, NumModules>(wpi::empty_array);
for (size_t index = 0; index < modulePositions.size(); index++) {
for (size_t index = 0; index < NumModules; index++) {
auto lastPosition = m_previousModulePositions[index];
auto currentPosition = modulePositions[index];
moduleDeltas[index] = {currentPosition.distance - lastPosition.distance,
currentPosition.angle};
m_previousModulePositions[index].distance = modulePositions[index].distance;
}
auto angle = gyroAngle + m_gyroOffset;
@@ -55,7 +64,6 @@ const Pose2d& frc::SwerveDriveOdometry<NumModules>::Update(
m_previousAngle = angle;
m_pose = {newPose.Translation(), angle};
m_previousModulePositions = modulePositions;
return m_pose;
}

217
wpimath/src/test/java/edu/wpi/first/math/estimator/DifferentialDrivePoseEstimatorTest.java
View File

@@ -6,35 +6,37 @@ package edu.wpi.first.math.estimator;
import static org.junit.jupiter.api.Assertions.assertEquals;
import edu.wpi.first.math.MatBuilder;
import edu.wpi.first.math.Nat;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.geometry.Transform2d;
import edu.wpi.first.math.geometry.Translation2d;
import edu.wpi.first.math.kinematics.ChassisSpeeds;
import edu.wpi.first.math.kinematics.DifferentialDriveKinematics;
import edu.wpi.first.math.kinematics.DifferentialDriveWheelSpeeds;
import edu.wpi.first.math.trajectory.Trajectory;
import edu.wpi.first.math.trajectory.TrajectoryConfig;
import edu.wpi.first.math.trajectory.TrajectoryGenerator;
import java.util.List;
import java.util.Random;
import java.util.TreeMap;
import java.util.function.Function;
import org.junit.jupiter.api.Test;
class DifferentialDrivePoseEstimatorTest {
@Test
void testAccuracy() {
var kinematics = new DifferentialDriveKinematics(1);
var estimator =
new DifferentialDrivePoseEstimator(
kinematics,
new Rotation2d(),
0,
0,
new Pose2d(),
new MatBuilder<>(Nat.N5(), Nat.N1()).fill(0.02, 0.02, 0.01, 0.02, 0.02),
new MatBuilder<>(Nat.N3(), Nat.N1()).fill(0.01, 0.01, 0.001),
new MatBuilder<>(Nat.N3(), Nat.N1()).fill(0.1, 0.1, 0.01));
var traj =
VecBuilder.fill(0.02, 0.02, 0.01),
VecBuilder.fill(0.1, 0.1, 0.1));
var trajectory =
TrajectoryGenerator.generateTrajectory(
List.of(
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
@@ -42,67 +44,165 @@ class DifferentialDrivePoseEstimatorTest {
new Pose2d(0, 0, Rotation2d.fromDegrees(135)),
new Pose2d(-3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(45))),
new TrajectoryConfig(10, 5));
new TrajectoryConfig(2, 2));
testFollowTrajectory(
kinematics,
estimator,
trajectory,
state ->
new ChassisSpeeds(
state.velocityMetersPerSecond,
0,
state.velocityMetersPerSecond * state.curvatureRadPerMeter),
state -> state.poseMeters,
trajectory.getInitialPose(),
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
0.02,
0.1,
0.25,
true);
}
@Test
void testBadInitialPose() {
var kinematics = new DifferentialDriveKinematics(1);
var rand = new Random(4915);
final double dt = 0.02;
var estimator =
new DifferentialDrivePoseEstimator(
kinematics,
new Rotation2d(),
0,
0,
new Pose2d(),
VecBuilder.fill(0.02, 0.02, 0.01),
VecBuilder.fill(0.1, 0.1, 0.1));
var trajectory =
TrajectoryGenerator.generateTrajectory(
List.of(
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
new Pose2d(3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(135)),
new Pose2d(-3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(45))),
new TrajectoryConfig(2, 2));
for (int offset_direction_degs = 0; offset_direction_degs < 360; offset_direction_degs += 45) {
for (int offset_heading_degs = 0; offset_heading_degs < 360; offset_heading_degs += 45) {
var pose_offset = Rotation2d.fromDegrees(offset_direction_degs);
var heading_offset = Rotation2d.fromDegrees(offset_heading_degs);
var initial_pose =
trajectory
.getInitialPose()
.plus(
new Transform2d(
new Translation2d(pose_offset.getCos(), pose_offset.getSin()),
heading_offset));
testFollowTrajectory(
kinematics,
estimator,
trajectory,
state ->
new ChassisSpeeds(
state.velocityMetersPerSecond,
0,
state.velocityMetersPerSecond * state.curvatureRadPerMeter),
state -> state.poseMeters,
initial_pose,
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
0.02,
0.1,
0.25,
false);
}
}
}
void testFollowTrajectory(
final DifferentialDriveKinematics kinematics,
final DifferentialDrivePoseEstimator estimator,
final Trajectory trajectory,
final Function<Trajectory.State, ChassisSpeeds> chassisSpeedsGenerator,
final Function<Trajectory.State, Pose2d> visionMeasurementGenerator,
final Pose2d startingPose,
final Pose2d endingPose,
final double dt,
final double visionUpdateRate,
final double visionUpdateDelay,
final boolean checkError) {
double leftDistanceMeters = 0;
double rightDistanceMeters = 0;
estimator.resetPosition(
new Rotation2d(), leftDistanceMeters, rightDistanceMeters, startingPose);
var rand = new Random(3538);
double t = 0.0;
final double visionUpdateRate = 0.1;
Pose2d lastVisionPose = null;
double lastVisionUpdateTime = Double.NEGATIVE_INFINITY;
System.out.print("time, est_x, est_y, est_theta, true_x, true_y, true_theta\n");
double distanceLeft = 0.0;
double distanceRight = 0.0;
final TreeMap<Double, Pose2d> visionUpdateQueue = new TreeMap<>();
double maxError = Double.NEGATIVE_INFINITY;
double errorSum = 0;
Trajectory.State groundtruthState;
DifferentialDriveWheelSpeeds input;
while (t <= traj.getTotalTimeSeconds()) {
groundtruthState = traj.sample(t);
input =
kinematics.toWheelSpeeds(
new ChassisSpeeds(
groundtruthState.velocityMetersPerSecond,
0.0,
// ds/dt * dtheta/ds = dtheta/dt
groundtruthState.velocityMetersPerSecond
* groundtruthState.curvatureRadPerMeter));
while (t <= trajectory.getTotalTimeSeconds()) {
var groundTruthState = trajectory.sample(t);
if (lastVisionUpdateTime + visionUpdateRate + rand.nextGaussian() * 0.4 < t) {
if (lastVisionPose != null) {
estimator.addVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
var groundPose = groundtruthState.poseMeters;
lastVisionPose =
new Pose2d(
new Translation2d(
groundPose.getTranslation().getX() + rand.nextGaussian() * 0.1,
groundPose.getTranslation().getY() + rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.01).plus(groundPose.getRotation()));
lastVisionUpdateTime = t;
// We are due for a new vision measurement if it's been `visionUpdateRate` seconds since the
// last vision measurement
if (visionUpdateQueue.isEmpty() || visionUpdateQueue.lastKey() + visionUpdateRate < t) {
Pose2d newVisionPose =
visionMeasurementGenerator
.apply(groundTruthState)
.plus(
new Transform2d(
new Translation2d(rand.nextGaussian() * 0.1, rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.05)));
visionUpdateQueue.put(t, newVisionPose);
}
input.leftMetersPerSecond += rand.nextGaussian() * 0.01;
input.rightMetersPerSecond += rand.nextGaussian() * 0.01;
// We should apply the oldest vision measurement if it has been `visionUpdateDelay` seconds
// since it was measured
if (!visionUpdateQueue.isEmpty() && visionUpdateQueue.firstKey() + visionUpdateDelay < t) {
var visionEntry = visionUpdateQueue.pollFirstEntry();
estimator.addVisionMeasurement(visionEntry.getValue(), visionEntry.getKey());
}
distanceLeft += input.leftMetersPerSecond * dt;
distanceRight += input.rightMetersPerSecond * dt;
var chassisSpeeds = chassisSpeedsGenerator.apply(groundTruthState);
var wheelSpeeds = kinematics.toWheelSpeeds(chassisSpeeds);
leftDistanceMeters += wheelSpeeds.leftMetersPerSecond * dt;
rightDistanceMeters += wheelSpeeds.rightMetersPerSecond * dt;
var rotNoise = new Rotation2d(rand.nextGaussian() * 0.001);
var xHat =
estimator.updateWithTime(
t,
groundtruthState.poseMeters.getRotation().plus(rotNoise),
input,
distanceLeft,
distanceRight);
groundTruthState
.poseMeters
.getRotation()
.plus(new Rotation2d(rand.nextGaussian() * 0.05))
.minus(trajectory.getInitialPose().getRotation()),
leftDistanceMeters,
rightDistanceMeters);
System.out.printf(
"%f, %f, %f, %f, %f, %f, %f\n",
t,
xHat.getX(),
xHat.getY(),
xHat.getRotation().getRadians(),
groundTruthState.poseMeters.getX(),
groundTruthState.poseMeters.getY(),
groundTruthState.poseMeters.getRotation().getRadians());
double error =
groundtruthState.poseMeters.getTranslation().getDistance(xHat.getTranslation());
groundTruthState.poseMeters.getTranslation().getDistance(xHat.getTranslation());
if (error > maxError) {
maxError = error;
}
@@ -111,7 +211,20 @@ class DifferentialDrivePoseEstimatorTest {
t += dt;
}
assertEquals(0.0, errorSum / (traj.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");
assertEquals(
endingPose.getX(), estimator.getEstimatedPosition().getX(), 0.08, "Incorrect Final X");
assertEquals(
endingPose.getY(), estimator.getEstimatedPosition().getY(), 0.08, "Incorrect Final Y");
assertEquals(
endingPose.getRotation().getRadians(),
estimator.getEstimatedPosition().getRotation().getRadians(),
0.15,
"Incorrect Final Theta");
if (checkError) {
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.07, "Incorrect mean error");
assertEquals(0.0, maxError, 0.2, "Incorrect max error");
}
}
}

300
wpimath/src/test/java/edu/wpi/first/math/estimator/MecanumDrivePoseEstimatorTest.java
View File

@@ -9,14 +9,18 @@ import static org.junit.jupiter.api.Assertions.assertEquals;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.geometry.Transform2d;
import edu.wpi.first.math.geometry.Translation2d;
import edu.wpi.first.math.kinematics.ChassisSpeeds;
import edu.wpi.first.math.kinematics.MecanumDriveKinematics;
import edu.wpi.first.math.kinematics.MecanumDriveWheelPositions;
import edu.wpi.first.math.trajectory.Trajectory;
import edu.wpi.first.math.trajectory.TrajectoryConfig;
import edu.wpi.first.math.trajectory.TrajectoryGenerator;
import java.util.List;
import java.util.Random;
import java.util.TreeMap;
import java.util.function.Function;
import org.junit.jupiter.api.Test;
class MecanumDrivePoseEstimatorTest {
@@ -31,68 +35,155 @@ class MecanumDrivePoseEstimatorTest {
var estimator =
new MecanumDrivePoseEstimator(
kinematics,
new Rotation2d(),
wheelPositions,
new Pose2d(),
kinematics,
VecBuilder.fill(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1),
VecBuilder.fill(0.05, 0.05, 0.05, 0.05, 0.05),
VecBuilder.fill(0.1, 0.1, 0.1));
VecBuilder.fill(0.1, 0.1, 0.1),
VecBuilder.fill(0.45, 0.45, 0.1));
var trajectory =
TrajectoryGenerator.generateTrajectory(
List.of(
new Pose2d(),
new Pose2d(20, 20, Rotation2d.fromDegrees(0)),
new Pose2d(10, 10, Rotation2d.fromDegrees(180)),
new Pose2d(30, 30, Rotation2d.fromDegrees(0)),
new Pose2d(20, 20, Rotation2d.fromDegrees(180)),
new Pose2d(10, 10, Rotation2d.fromDegrees(0))),
new TrajectoryConfig(0.5, 2));
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
new Pose2d(3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(135)),
new Pose2d(-3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(45))),
new TrajectoryConfig(2, 2));
var rand = new Random(5190);
testFollowTrajectory(
kinematics,
estimator,
trajectory,
state ->
new ChassisSpeeds(
state.velocityMetersPerSecond,
0,
state.velocityMetersPerSecond * state.curvatureRadPerMeter),
state -> state.poseMeters,
trajectory.getInitialPose(),
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
0.02,
0.1,
0.25,
true);
}
@Test
void testBadInitialPose() {
var kinematics =
new MecanumDriveKinematics(
new Translation2d(1, 1), new Translation2d(1, -1),
new Translation2d(-1, -1), new Translation2d(-1, 1));
var wheelPositions = new MecanumDriveWheelPositions();
var estimator =
new MecanumDrivePoseEstimator(
kinematics,
new Rotation2d(),
wheelPositions,
new Pose2d(),
VecBuilder.fill(0.1, 0.1, 0.1),
VecBuilder.fill(0.45, 0.45, 0.1));
var trajectory =
TrajectoryGenerator.generateTrajectory(
List.of(
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
new Pose2d(3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(135)),
new Pose2d(-3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(45))),
new TrajectoryConfig(2, 2));
for (int offset_direction_degs = 0; offset_direction_degs < 360; offset_direction_degs += 45) {
for (int offset_heading_degs = 0; offset_heading_degs < 360; offset_heading_degs += 45) {
var pose_offset = Rotation2d.fromDegrees(offset_direction_degs);
var heading_offset = Rotation2d.fromDegrees(offset_heading_degs);
var initial_pose =
trajectory
.getInitialPose()
.plus(
new Transform2d(
new Translation2d(pose_offset.getCos(), pose_offset.getSin()),
heading_offset));
testFollowTrajectory(
kinematics,
estimator,
trajectory,
state ->
new ChassisSpeeds(
state.velocityMetersPerSecond,
0,
state.velocityMetersPerSecond * state.curvatureRadPerMeter),
state -> state.poseMeters,
initial_pose,
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
0.02,
0.1,
0.25,
false);
}
}
}
void testFollowTrajectory(
final MecanumDriveKinematics kinematics,
final MecanumDrivePoseEstimator estimator,
final Trajectory trajectory,
final Function<Trajectory.State, ChassisSpeeds> chassisSpeedsGenerator,
final Function<Trajectory.State, Pose2d> visionMeasurementGenerator,
final Pose2d startingPose,
final Pose2d endingPose,
final double dt,
final double visionUpdateRate,
final double visionUpdateDelay,
final boolean checkError) {
var wheelPositions = new MecanumDriveWheelPositions();
estimator.resetPosition(new Rotation2d(), wheelPositions, startingPose);
var rand = new Random(3538);
final double dt = 0.02;
double t = 0.0;
final double visionUpdateRate = 0.1;
Pose2d lastVisionPose = null;
double lastVisionUpdateTime = Double.NEGATIVE_INFINITY;
System.out.print("time, est_x, est_y, est_theta, true_x, true_y, true_theta\n");
final TreeMap<Double, Pose2d> visionUpdateQueue = new TreeMap<>();
double maxError = Double.NEGATIVE_INFINITY;
double errorSum = 0;
while (t <= trajectory.getTotalTimeSeconds()) {
var groundTruthState = trajectory.sample(t);
if (lastVisionUpdateTime + visionUpdateRate < t) {
if (lastVisionPose != null) {
estimator.addVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
// We are due for a new vision measurement if it's been `visionUpdateRate` seconds since the
// last vision measurement
if (visionUpdateQueue.isEmpty() || visionUpdateQueue.lastKey() + visionUpdateRate < t) {
Pose2d newVisionPose =
visionMeasurementGenerator
.apply(groundTruthState)
.plus(
new Transform2d(
new Translation2d(rand.nextGaussian() * 0.1, rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.05)));
lastVisionPose =
new Pose2d(
new Translation2d(
groundTruthState.poseMeters.getTranslation().getX() + rand.nextGaussian() * 0.1,
groundTruthState.poseMeters.getTranslation().getY()
+ rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.1)
.plus(groundTruthState.poseMeters.getRotation()));
lastVisionUpdateTime = t;
visionUpdateQueue.put(t, newVisionPose);
}
var wheelSpeeds =
kinematics.toWheelSpeeds(
new ChassisSpeeds(
groundTruthState.velocityMetersPerSecond,
0,
groundTruthState.velocityMetersPerSecond
* groundTruthState.curvatureRadPerMeter));
// We should apply the oldest vision measurement if it has been `visionUpdateDelay` seconds
// since it was measured
if (!visionUpdateQueue.isEmpty() && visionUpdateQueue.firstKey() + visionUpdateDelay < t) {
var visionEntry = visionUpdateQueue.pollFirstEntry();
estimator.addVisionMeasurement(visionEntry.getValue(), visionEntry.getKey());
}
wheelSpeeds.frontLeftMetersPerSecond += rand.nextGaussian() * 0.1;
wheelSpeeds.frontRightMetersPerSecond += rand.nextGaussian() * 0.1;
wheelSpeeds.rearLeftMetersPerSecond += rand.nextGaussian() * 0.1;
wheelSpeeds.rearRightMetersPerSecond += rand.nextGaussian() * 0.1;
var chassisSpeeds = chassisSpeedsGenerator.apply(groundTruthState);
var wheelSpeeds = kinematics.toWheelSpeeds(chassisSpeeds);
wheelPositions.frontLeftMeters += wheelSpeeds.frontLeftMetersPerSecond * dt;
wheelPositions.frontRightMeters += wheelSpeeds.frontRightMetersPerSecond * dt;
@@ -105,108 +196,19 @@ class MecanumDrivePoseEstimatorTest {
groundTruthState
.poseMeters
.getRotation()
.plus(new Rotation2d(rand.nextGaussian() * 0.05)),
wheelSpeeds,
.plus(new Rotation2d(rand.nextGaussian() * 0.05))
.minus(trajectory.getInitialPose().getRotation()),
wheelPositions);
double error =
groundTruthState.poseMeters.getTranslation().getDistance(xHat.getTranslation());
if (error > maxError) {
maxError = error;
}
errorSum += error;
t += dt;
}
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");
}
@Test
void testAccuracyFacingXAxis() {
var kinematics =
new MecanumDriveKinematics(
new Translation2d(1, 1), new Translation2d(1, -1),
new Translation2d(-1, -1), new Translation2d(-1, 1));
var wheelPositions = new MecanumDriveWheelPositions();
var estimator =
new MecanumDrivePoseEstimator(
new Rotation2d(),
wheelPositions,
new Pose2d(),
kinematics,
VecBuilder.fill(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1),
VecBuilder.fill(0.05, 0.05, 0.05, 0.05, 0.05),
VecBuilder.fill(0.1, 0.1, 0.1));
var trajectory =
TrajectoryGenerator.generateTrajectory(
List.of(
new Pose2d(),
new Pose2d(20, 20, Rotation2d.fromDegrees(0)),
new Pose2d(10, 10, Rotation2d.fromDegrees(180)),
new Pose2d(30, 30, Rotation2d.fromDegrees(0)),
new Pose2d(20, 20, Rotation2d.fromDegrees(180)),
new Pose2d(10, 10, Rotation2d.fromDegrees(0))),
new TrajectoryConfig(0.5, 2));
var rand = new Random(5190);
final double dt = 0.02;
double t = 0.0;
final double visionUpdateRate = 0.1;
Pose2d lastVisionPose = null;
double lastVisionUpdateTime = Double.NEGATIVE_INFINITY;
double maxError = Double.NEGATIVE_INFINITY;
double errorSum = 0;
while (t <= trajectory.getTotalTimeSeconds()) {
var groundTruthState = trajectory.sample(t);
if (lastVisionUpdateTime + visionUpdateRate < t) {
if (lastVisionPose != null) {
estimator.addVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
new Pose2d(
new Translation2d(
groundTruthState.poseMeters.getTranslation().getX() + rand.nextGaussian() * 0.1,
groundTruthState.poseMeters.getTranslation().getY()
+ rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.1)
.plus(groundTruthState.poseMeters.getRotation()));
lastVisionUpdateTime = t;
}
var wheelSpeeds =
kinematics.toWheelSpeeds(
new ChassisSpeeds(
groundTruthState.velocityMetersPerSecond
* groundTruthState.poseMeters.getRotation().getCos(),
groundTruthState.velocityMetersPerSecond
* groundTruthState.poseMeters.getRotation().getSin(),
0));
wheelSpeeds.frontLeftMetersPerSecond += rand.nextGaussian() * 0.1;
wheelSpeeds.frontRightMetersPerSecond += rand.nextGaussian() * 0.1;
wheelSpeeds.rearLeftMetersPerSecond += rand.nextGaussian() * 0.1;
wheelSpeeds.rearRightMetersPerSecond += rand.nextGaussian() * 0.1;
wheelPositions.frontLeftMeters += wheelSpeeds.frontLeftMetersPerSecond * dt;
wheelPositions.frontRightMeters += wheelSpeeds.frontRightMetersPerSecond * dt;
wheelPositions.rearLeftMeters += wheelSpeeds.rearLeftMetersPerSecond * dt;
wheelPositions.rearRightMeters += wheelSpeeds.rearRightMetersPerSecond * dt;
var xHat =
estimator.updateWithTime(
t, new Rotation2d(rand.nextGaussian() * 0.05), wheelSpeeds, wheelPositions);
System.out.printf(
"%f, %f, %f, %f, %f, %f, %f\n",
t,
xHat.getX(),
xHat.getY(),
xHat.getRotation().getRadians(),
groundTruthState.poseMeters.getX(),
groundTruthState.poseMeters.getY(),
groundTruthState.poseMeters.getRotation().getRadians());
double error =
groundTruthState.poseMeters.getTranslation().getDistance(xHat.getTranslation());
@@ -219,7 +221,19 @@ class MecanumDrivePoseEstimatorTest {
}
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");
endingPose.getX(), estimator.getEstimatedPosition().getX(), 0.08, "Incorrect Final X");
assertEquals(
endingPose.getY(), estimator.getEstimatedPosition().getY(), 0.08, "Incorrect Final Y");
assertEquals(
endingPose.getRotation().getRadians(),
estimator.getEstimatedPosition().getRotation().getRadians(),
0.15,
"Incorrect Final Theta");
if (checkError) {
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.07, "Incorrect mean error");
assertEquals(0.0, maxError, 0.2, "Incorrect max error");
}
}
}

307
wpimath/src/test/java/edu/wpi/first/math/estimator/SwerveDrivePoseEstimatorTest.java
View File

@@ -6,20 +6,21 @@ package edu.wpi.first.math.estimator;
import static org.junit.jupiter.api.Assertions.assertEquals;
import edu.wpi.first.math.Nat;
import edu.wpi.first.math.VecBuilder;
import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.geometry.Transform2d;
import edu.wpi.first.math.geometry.Translation2d;
import edu.wpi.first.math.kinematics.ChassisSpeeds;
import edu.wpi.first.math.kinematics.SwerveDriveKinematics;
import edu.wpi.first.math.kinematics.SwerveModulePosition;
import edu.wpi.first.math.numbers.N5;
import edu.wpi.first.math.numbers.N7;
import edu.wpi.first.math.trajectory.Trajectory;
import edu.wpi.first.math.trajectory.TrajectoryConfig;
import edu.wpi.first.math.trajectory.TrajectoryGenerator;
import java.util.List;
import java.util.Random;
import java.util.TreeMap;
import java.util.function.Function;
import org.junit.jupiter.api.Test;
class SwerveDrivePoseEstimatorTest {
@@ -38,17 +39,13 @@ class SwerveDrivePoseEstimatorTest {
var br = new SwerveModulePosition();
var estimator =
new SwerveDrivePoseEstimator<N7, N7, N5>(
Nat.N7(),
Nat.N7(),
Nat.N5(),
new SwerveDrivePoseEstimator(
kinematics,
new Rotation2d(),
new SwerveModulePosition[] {fl, fr, bl, br},
new Pose2d(),
kinematics,
VecBuilder.fill(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1),
VecBuilder.fill(0.005, 0.005, 0.005, 0.005, 0.005),
VecBuilder.fill(0.1, 0.1, 0.1));
VecBuilder.fill(0.1, 0.1, 0.1),
VecBuilder.fill(0.5, 0.5, 0.5));
var trajectory =
TrajectoryGenerator.generateTrajectory(
@@ -58,88 +55,28 @@ class SwerveDrivePoseEstimatorTest {
new Pose2d(0, 0, Rotation2d.fromDegrees(135)),
new Pose2d(-3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(45))),
new TrajectoryConfig(0.5, 2));
new TrajectoryConfig(2, 2));
var rand = new Random(4915);
final double dt = 0.02;
double t = 0.0;
final double visionUpdateRate = 0.1;
Pose2d lastVisionPose = null;
double lastVisionUpdateTime = Double.NEGATIVE_INFINITY;
double maxError = Double.NEGATIVE_INFINITY;
double errorSum = 0;
while (t <= trajectory.getTotalTimeSeconds()) {
var groundTruthState = trajectory.sample(t);
if (lastVisionUpdateTime + visionUpdateRate < t) {
if (lastVisionPose != null) {
estimator.addVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
new Pose2d(
new Translation2d(
groundTruthState.poseMeters.getTranslation().getX() + rand.nextGaussian() * 0.1,
groundTruthState.poseMeters.getTranslation().getY()
+ rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.1)
.plus(groundTruthState.poseMeters.getRotation()));
lastVisionUpdateTime = t;
}
var moduleStates =
kinematics.toSwerveModuleStates(
new ChassisSpeeds(
groundTruthState.velocityMetersPerSecond,
0.0,
groundTruthState.velocityMetersPerSecond
* groundTruthState.curvatureRadPerMeter));
for (var moduleState : moduleStates) {
moduleState.angle = moduleState.angle.plus(new Rotation2d(rand.nextGaussian() * 0.005));
moduleState.speedMetersPerSecond += rand.nextGaussian() * 0.1;
}
fl.distanceMeters += moduleStates[0].speedMetersPerSecond * dt;
fr.distanceMeters += moduleStates[1].speedMetersPerSecond * dt;
bl.distanceMeters += moduleStates[2].speedMetersPerSecond * dt;
br.distanceMeters += moduleStates[3].speedMetersPerSecond * dt;
fl.angle = moduleStates[0].angle;
fr.angle = moduleStates[1].angle;
bl.angle = moduleStates[2].angle;
br.angle = moduleStates[3].angle;
var xHat =
estimator.updateWithTime(
t,
groundTruthState
.poseMeters
.getRotation()
.plus(new Rotation2d(rand.nextGaussian() * 0.05)),
moduleStates,
new SwerveModulePosition[] {fl, fr, bl, br});
double error =
groundTruthState.poseMeters.getTranslation().getDistance(xHat.getTranslation());
if (error > maxError) {
maxError = error;
}
errorSum += error;
t += dt;
}
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");
testFollowTrajectory(
kinematics,
estimator,
trajectory,
state ->
new ChassisSpeeds(
state.velocityMetersPerSecond,
0,
state.velocityMetersPerSecond * state.curvatureRadPerMeter),
state -> state.poseMeters,
trajectory.getInitialPose(),
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
0.02,
0.1,
0.25,
true);
}
@Test
void testAccuracyFacingXAxis() {
void testBadInitialPose() {
var kinematics =
new SwerveDriveKinematics(
new Translation2d(1, 1),
@@ -153,18 +90,13 @@ class SwerveDrivePoseEstimatorTest {
var br = new SwerveModulePosition();
var estimator =
new SwerveDrivePoseEstimator<N7, N7, N5>(
Nat.N7(),
Nat.N7(),
Nat.N5(),
new SwerveDrivePoseEstimator(
kinematics,
new Rotation2d(),
new SwerveModulePosition[] {fl, fr, bl, br},
new Pose2d(),
kinematics,
VecBuilder.fill(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1),
VecBuilder.fill(0.005, 0.005, 0.005, 0.005, 0.005),
VecBuilder.fill(0.1, 0.1, 0.1));
new Pose2d(-1, -1, Rotation2d.fromRadians(-1)),
VecBuilder.fill(0.1, 0.1, 0.1),
VecBuilder.fill(0.9, 0.9, 0.9));
var trajectory =
TrajectoryGenerator.generateTrajectory(
List.of(
@@ -173,76 +105,137 @@ class SwerveDrivePoseEstimatorTest {
new Pose2d(0, 0, Rotation2d.fromDegrees(135)),
new Pose2d(-3, 0, Rotation2d.fromDegrees(-90)),
new Pose2d(0, 0, Rotation2d.fromDegrees(45))),
new TrajectoryConfig(0.5, 2));
new TrajectoryConfig(2, 2));
var rand = new Random(4915);
for (int offset_direction_degs = 0; offset_direction_degs < 360; offset_direction_degs += 45) {
for (int offset_heading_degs = 0; offset_heading_degs < 360; offset_heading_degs += 45) {
var pose_offset = Rotation2d.fromDegrees(offset_direction_degs);
var heading_offset = Rotation2d.fromDegrees(offset_heading_degs);
var initial_pose =
trajectory
.getInitialPose()
.plus(
new Transform2d(
new Translation2d(pose_offset.getCos(), pose_offset.getSin()),
heading_offset));
testFollowTrajectory(
kinematics,
estimator,
trajectory,
state ->
new ChassisSpeeds(
state.velocityMetersPerSecond,
0,
state.velocityMetersPerSecond * state.curvatureRadPerMeter),
state -> state.poseMeters,
initial_pose,
new Pose2d(0, 0, Rotation2d.fromDegrees(45)),
0.02,
0.1,
1.0,
false);
}
}
}
void testFollowTrajectory(
final SwerveDriveKinematics kinematics,
final SwerveDrivePoseEstimator estimator,
final Trajectory trajectory,
final Function<Trajectory.State, ChassisSpeeds> chassisSpeedsGenerator,
final Function<Trajectory.State, Pose2d> visionMeasurementGenerator,
final Pose2d startingPose,
final Pose2d endingPose,
final double dt,
final double visionUpdateRate,
final double visionUpdateDelay,
final boolean checkError) {
SwerveModulePosition[] positions = {
new SwerveModulePosition(),
new SwerveModulePosition(),
new SwerveModulePosition(),
new SwerveModulePosition()
};
estimator.resetPosition(new Rotation2d(), positions, startingPose);
var rand = new Random(3538);
final double dt = 0.02;
double t = 0.0;
final double visionUpdateRate = 0.1;
Pose2d lastVisionPose = null;
double lastVisionUpdateTime = Double.NEGATIVE_INFINITY;
System.out.print(
"time, est_x, est_y, est_theta, true_x, true_y, true_theta, "
+ "distance_1, distance_2, distance_3, distance_4, "
+ "angle_1, angle_2, angle_3, angle_4\n");
final TreeMap<Double, Pose2d> visionUpdateQueue = new TreeMap<>();
double maxError = Double.NEGATIVE_INFINITY;
double errorSum = 0;
while (t <= trajectory.getTotalTimeSeconds()) {
var groundTruthState = trajectory.sample(t);
if (lastVisionUpdateTime + visionUpdateRate < t) {
if (lastVisionPose != null) {
estimator.addVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
// We are due for a new vision measurement if it's been `visionUpdateRate` seconds since the
// last vision measurement
if (visionUpdateQueue.isEmpty() || visionUpdateQueue.lastKey() + visionUpdateRate < t) {
Pose2d newVisionPose =
visionMeasurementGenerator
.apply(groundTruthState)
.plus(
new Transform2d(
new Translation2d(rand.nextGaussian() * 0.1, rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.05)));
lastVisionPose =
new Pose2d(
new Translation2d(
groundTruthState.poseMeters.getTranslation().getX() + rand.nextGaussian() * 0.1,
groundTruthState.poseMeters.getTranslation().getY()
+ rand.nextGaussian() * 0.1),
new Rotation2d(rand.nextGaussian() * 0.1)
.plus(groundTruthState.poseMeters.getRotation()));
lastVisionUpdateTime = t;
visionUpdateQueue.put(t, newVisionPose);
}
var moduleStates =
kinematics.toSwerveModuleStates(
new ChassisSpeeds(
groundTruthState.velocityMetersPerSecond
* groundTruthState.poseMeters.getRotation().getCos(),
groundTruthState.velocityMetersPerSecond
* groundTruthState.poseMeters.getRotation().getSin(),
0.0));
for (var moduleState : moduleStates) {
moduleState.angle = moduleState.angle.plus(new Rotation2d(rand.nextGaussian() * 0.005));
moduleState.speedMetersPerSecond += rand.nextGaussian() * 0.1;
// We should apply the oldest vision measurement if it has been `visionUpdateDelay` seconds
// since it was measured
if (!visionUpdateQueue.isEmpty() && visionUpdateQueue.firstKey() + visionUpdateDelay < t) {
var visionEntry = visionUpdateQueue.pollFirstEntry();
estimator.addVisionMeasurement(visionEntry.getValue(), visionEntry.getKey());
}
fl.distanceMeters +=
groundTruthState.velocityMetersPerSecond * dt
+ 0.5 * groundTruthState.accelerationMetersPerSecondSq * dt * dt;
fr.distanceMeters +=
groundTruthState.velocityMetersPerSecond * dt
+ 0.5 * groundTruthState.accelerationMetersPerSecondSq * dt * dt;
bl.distanceMeters +=
groundTruthState.velocityMetersPerSecond * dt
+ 0.5 * groundTruthState.accelerationMetersPerSecondSq * dt * dt;
br.distanceMeters +=
groundTruthState.velocityMetersPerSecond * dt
+ 0.5 * groundTruthState.accelerationMetersPerSecondSq * dt * dt;
var chassisSpeeds = chassisSpeedsGenerator.apply(groundTruthState);
fl.angle = groundTruthState.poseMeters.getRotation();
fr.angle = groundTruthState.poseMeters.getRotation();
bl.angle = groundTruthState.poseMeters.getRotation();
br.angle = groundTruthState.poseMeters.getRotation();
var moduleStates = kinematics.toSwerveModuleStates(chassisSpeeds);
for (int i = 0; i < moduleStates.length; i++) {
positions[i].distanceMeters +=
moduleStates[i].speedMetersPerSecond * (1 - rand.nextGaussian() * 0.05) * dt;
positions[i].angle =
moduleStates[i].angle.plus(new Rotation2d(rand.nextGaussian() * 0.005));
}
var xHat =
estimator.updateWithTime(
t,
new Rotation2d(rand.nextGaussian() * 0.05),
moduleStates,
new SwerveModulePosition[] {fl, fr, bl, br});
groundTruthState
.poseMeters
.getRotation()
.plus(new Rotation2d(rand.nextGaussian() * 0.05))
.minus(trajectory.getInitialPose().getRotation()),
positions);
System.out.printf(
"%f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f, %f\n",
t,
xHat.getX(),
xHat.getY(),
xHat.getRotation().getRadians(),
groundTruthState.poseMeters.getX(),
groundTruthState.poseMeters.getY(),
groundTruthState.poseMeters.getRotation().getRadians(),
positions[0].distanceMeters,
positions[1].distanceMeters,
positions[2].distanceMeters,
positions[3].distanceMeters,
positions[0].angle.getRadians(),
positions[1].angle.getRadians(),
positions[2].angle.getRadians(),
positions[3].angle.getRadians());
double error =
groundTruthState.poseMeters.getTranslation().getDistance(xHat.getTranslation());
@@ -255,7 +248,19 @@ class SwerveDrivePoseEstimatorTest {
}
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");
endingPose.getX(), estimator.getEstimatedPosition().getX(), 0.08, "Incorrect Final X");
assertEquals(
endingPose.getY(), estimator.getEstimatedPosition().getY(), 0.08, "Incorrect Final Y");
assertEquals(
endingPose.getRotation().getRadians(),
estimator.getEstimatedPosition().getRotation().getRadians(),
0.15,
"Incorrect Final Theta");
if (checkError) {
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.07, "Incorrect mean error");
assertEquals(0.0, maxError, 0.2, "Incorrect max error");
}
}
}

16
wpimath/src/test/java/edu/wpi/first/math/kinematics/SwerveDriveOdometryTest.java
View File

@@ -178,6 +178,14 @@ class SwerveDriveOdometryTest {
t += dt;
}
assertEquals(0.0, odometry.getPoseMeters().getX(), 1e-1, "Incorrect Final X");
assertEquals(0.0, odometry.getPoseMeters().getY(), 1e-1, "Incorrect Final Y");
assertEquals(
Math.PI / 4,
odometry.getPoseMeters().getRotation().getRadians(),
10 * Math.PI / 180,
"Incorrect Final Theta");
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");
@@ -253,6 +261,14 @@ class SwerveDriveOdometryTest {
t += dt;
}
assertEquals(0.0, odometry.getPoseMeters().getX(), 1e-1, "Incorrect Final X");
assertEquals(0.0, odometry.getPoseMeters().getY(), 1e-1, "Incorrect Final Y");
assertEquals(
0.0,
odometry.getPoseMeters().getRotation().getRadians(),
10 * Math.PI / 180,
"Incorrect Final Theta");
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.06, "Incorrect mean error");
assertEquals(0.0, maxError, 0.125, "Incorrect max error");

206
wpimath/src/test/native/cpp/estimator/DifferentialDrivePoseEstimatorTest.cpp
View File

@@ -4,6 +4,8 @@
#include <limits>
#include <random>
#include <tuple>
#include <utility>
#include "frc/StateSpaceUtil.h"
#include "frc/estimator/DifferentialDrivePoseEstimator.h"
@@ -16,68 +18,89 @@
#include "units/length.h"
#include "units/time.h"
TEST(DifferentialDrivePoseEstimatorTest, Accuracy) {
frc::DifferentialDrivePoseEstimator estimator{frc::Rotation2d{},
0_m,
0_m,
frc::Pose2d{},
{0.02, 0.02, 0.01, 0.02, 0.02},
{0.01, 0.01, 0.001},
{0.1, 0.1, 0.01}};
void testFollowTrajectory(
const frc::DifferentialDriveKinematics& kinematics,
frc::DifferentialDrivePoseEstimator& estimator,
const frc::Trajectory& trajectory,
std::function<frc::ChassisSpeeds(frc::Trajectory::State&)>
chassisSpeedsGenerator,
std::function<frc::Pose2d(frc::Trajectory::State&)>
visionMeasurementGenerator,
const frc::Pose2d& startingPose, const frc::Pose2d& endingPose,
const units::second_t dt, const units::second_t kVisionUpdateRate,
const units::second_t kVisionUpdateDelay, const bool checkError,
const bool debug) {
units::meter_t leftDistance = 0_m;
units::meter_t rightDistance = 0_m;
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(10_mps, 5.0_mps_sq));
frc::DifferentialDriveKinematics kinematics{1.0_m};
estimator.ResetPosition(frc::Rotation2d{}, leftDistance, rightDistance,
startingPose);
std::default_random_engine generator;
std::normal_distribution<double> distribution(0.0, 1.0);
units::second_t dt = 0.02_s;
units::second_t t = 0.0_s;
units::second_t t = 0_s;
units::meter_t leftDistance = 0_m;
units::meter_t rightDistance = 0_m;
units::second_t kVisionUpdateRate = 0.1_s;
frc::Pose2d lastVisionPose;
units::second_t lastVisionUpdateTime{-std::numeric_limits<double>::max()};
std::vector<std::pair<units::second_t, frc::Pose2d>> visionPoses;
std::vector<std::tuple<units::second_t, units::second_t, frc::Pose2d>>
visionLog;
double maxError = -std::numeric_limits<double>::max();
double errorSum = 0;
while (t <= trajectory.TotalTime()) {
auto groundTruthState = trajectory.Sample(t);
auto input = kinematics.ToWheelSpeeds(
{groundTruthState.velocity, 0_mps,
groundTruthState.velocity * groundTruthState.curvature});
if (debug) {
fmt::print(
"time, est_x, est_y, est_theta, true_x, true_y, true_theta, left, "
"right\n");
}
if (lastVisionUpdateTime + kVisionUpdateRate < t) {
if (lastVisionPose != frc::Pose2d{}) {
estimator.AddVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
groundTruthState.pose +
frc::Transform2d{
frc::Translation2d{distribution(generator) * 0.1 * 1_m,
distribution(generator) * 0.1 * 1_m},
frc::Rotation2d{distribution(generator) * 0.01 * 1_rad}};
while (t < trajectory.TotalTime()) {
frc::Trajectory::State groundTruthState = trajectory.Sample(t);
lastVisionUpdateTime = t;
// We are due for a new vision measurement if it's been `visionUpdateRate`
// seconds since the last vision measurement
if (visionPoses.empty() ||
visionPoses.back().first + kVisionUpdateRate < t) {
auto visionPose =
visionMeasurementGenerator(groundTruthState) +
frc::Transform2d{frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.05_rad}};
visionPoses.push_back({t, visionPose});
}
leftDistance += input.left * distribution(generator) * 0.01 * dt;
rightDistance += input.right * distribution(generator) * 0.01 * dt;
// We should apply the oldest vision measurement if it has been
// `visionUpdateDelay` seconds since it was measured
if (!visionPoses.empty() &&
visionPoses.front().first + kVisionUpdateDelay < t) {
auto visionEntry = visionPoses.front();
estimator.AddVisionMeasurement(visionEntry.second, visionEntry.first);
visionPoses.erase(visionPoses.begin());
visionLog.push_back({t, visionEntry.first, visionEntry.second});
}
auto chassisSpeeds = chassisSpeedsGenerator(groundTruthState);
auto wheelSpeeds = kinematics.ToWheelSpeeds(chassisSpeeds);
leftDistance += wheelSpeeds.left * dt;
rightDistance += wheelSpeeds.right * dt;
auto xhat = estimator.UpdateWithTime(
t,
groundTruthState.pose.Rotation() +
frc::Rotation2d{units::radian_t{distribution(generator) * 0.001}},
input, leftDistance, rightDistance);
frc::Rotation2d{distribution(generator) * 0.05_rad} -
trajectory.InitialPose().Rotation(),
leftDistance, rightDistance);
if (debug) {
fmt::print(
"{}, {}, {}, {}, {}, {}, {}, {}, {}\n", t.value(), xhat.X().value(),
xhat.Y().value(), xhat.Rotation().Radians().value(),
groundTruthState.pose.X().value(), groundTruthState.pose.Y().value(),
groundTruthState.pose.Rotation().Radians().value(),
leftDistance.value(), rightDistance.value());
}
double error = groundTruthState.pose.Translation()
.Distance(xhat.Translation())
@@ -91,7 +114,96 @@ TEST(DifferentialDrivePoseEstimatorTest, Accuracy) {
t += dt;
}
EXPECT_NEAR(0.0, errorSum / (trajectory.TotalTime().value() / dt.value()),
0.05);
EXPECT_NEAR(0.0, maxError, 0.125);
if (debug) {
fmt::print("apply_time, measured_time, vision_x, vision_y, vision_theta\n");
units::second_t apply_time;
units::second_t measure_time;
frc::Pose2d vision_pose;
for (auto record : visionLog) {
std::tie(apply_time, measure_time, vision_pose) = record;
fmt::print("{}, {}, {}, {}, {}\n", apply_time.value(),
measure_time.value(), vision_pose.X().value(),
vision_pose.Y().value(),
vision_pose.Rotation().Radians().value());
}
}
EXPECT_NEAR(endingPose.X().value(),
estimator.GetEstimatedPosition().X().value(), 0.08);
EXPECT_NEAR(endingPose.Y().value(),
estimator.GetEstimatedPosition().Y().value(), 0.08);
EXPECT_NEAR(endingPose.Rotation().Radians().value(),
estimator.GetEstimatedPosition().Rotation().Radians().value(),
0.15);
if (checkError) {
EXPECT_LT(errorSum / (trajectory.TotalTime() / dt), 0.05);
EXPECT_LT(maxError, 0.2);
}
}
TEST(DifferentialDrivePoseEstimatorTest, Accuracy) {
frc::DifferentialDriveKinematics kinematics{1.0_m};
frc::DifferentialDrivePoseEstimator estimator{
kinematics, frc::Rotation2d{}, 0_m, 0_m, frc::Pose2d{},
{0.02, 0.02, 0.01}, {0.1, 0.1, 0.1}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(2_mps, 2_mps_sq));
testFollowTrajectory(
kinematics, estimator, trajectory,
[&](frc::Trajectory::State& state) {
return frc::ChassisSpeeds{state.velocity, 0_mps,
state.velocity * state.curvature};
},
[&](frc::Trajectory::State& state) { return state.pose; },
trajectory.InitialPose(), {0_m, 0_m, frc::Rotation2d{45_deg}}, 0.02_s,
0.1_s, 0.25_s, true, false);
}
TEST(DifferentialDrivePoseEstimatorTest, BadInitialPose) {
frc::DifferentialDriveKinematics kinematics{1.0_m};
frc::DifferentialDrivePoseEstimator estimator{
kinematics, frc::Rotation2d{}, 0_m, 0_m, frc::Pose2d{},
{0.02, 0.02, 0.01}, {0.1, 0.1, 0.1}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(2_mps, 2_mps_sq));
for (units::degree_t offset_direction_degs = 0_deg;
offset_direction_degs < 360_deg; offset_direction_degs += 45_deg) {
for (units::degree_t offset_heading_degs = 0_deg;
offset_heading_degs < 360_deg; offset_heading_degs += 45_deg) {
auto pose_offset = frc::Rotation2d{offset_direction_degs};
auto heading_offset = frc::Rotation2d{offset_heading_degs};
auto initial_pose =
trajectory.InitialPose() +
frc::Transform2d{frc::Translation2d{pose_offset.Cos() * 1_m,
pose_offset.Sin() * 1_m},
heading_offset};
testFollowTrajectory(
kinematics, estimator, trajectory,
[&](frc::Trajectory::State& state) {
return frc::ChassisSpeeds{state.velocity, 0_mps,
state.velocity * state.curvature};
},
[&](frc::Trajectory::State& state) { return state.pose; },
initial_pose, {0_m, 0_m, frc::Rotation2d{45_deg}}, 0.02_s, 0.1_s,
0.25_s, false, false);
}
}
}

255
wpimath/src/test/native/cpp/estimator/MecanumDrivePoseEstimatorTest.cpp
View File

@@ -4,6 +4,7 @@
#include <limits>
#include <random>
#include <tuple>
#include "frc/estimator/MecanumDrivePoseEstimator.h"
#include "frc/geometry/Pose2d.h"
@@ -11,63 +12,66 @@
#include "frc/trajectory/TrajectoryGenerator.h"
#include "gtest/gtest.h"
TEST(MecanumDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
frc::MecanumDriveKinematics kinematics{
frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
frc::Translation2d{-1_m, -1_m}, frc::Translation2d{-1_m, 1_m}};
void testFollowTrajectory(
const frc::MecanumDriveKinematics& kinematics,
frc::MecanumDrivePoseEstimator& estimator,
const frc::Trajectory& trajectory,
std::function<frc::ChassisSpeeds(frc::Trajectory::State&)>
chassisSpeedsGenerator,
std::function<frc::Pose2d(frc::Trajectory::State&)>
visionMeasurementGenerator,
const frc::Pose2d& startingPose, const frc::Pose2d& endingPose,
const units::second_t dt, const units::second_t kVisionUpdateRate,
const units::second_t kVisionUpdateDelay, const bool checkError,
const bool debug) {
frc::MecanumDriveWheelPositions wheelPositions{};
frc::MecanumDriveWheelPositions wheelPositions;
frc::MecanumDrivePoseEstimator estimator{frc::Rotation2d{},
wheelPositions,
frc::Pose2d{},
kinematics,
{0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1},
{0.05, 0.05, 0.05, 0.05, 0.05},
{0.1, 0.1, 0.1}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(5.0_mps, 2.0_mps_sq));
estimator.ResetPosition(frc::Rotation2d{}, wheelPositions, startingPose);
std::default_random_engine generator;
std::normal_distribution<double> distribution(0.0, 1.0);
units::second_t dt = 0.02_s;
units::second_t t = 0_s;
units::second_t kVisionUpdateRate = 0.1_s;
frc::Pose2d lastVisionPose;
units::second_t lastVisionUpdateTime{-std::numeric_limits<double>::max()};
std::vector<frc::Pose2d> visionPoses;
std::vector<std::pair<units::second_t, frc::Pose2d>> visionPoses;
std::vector<std::tuple<units::second_t, units::second_t, frc::Pose2d>>
visionLog;
double maxError = -std::numeric_limits<double>::max();
double errorSum = 0;
if (debug) {
fmt::print("time, est_x, est_y, est_theta, true_x, true_y, true_theta\n");
}
while (t < trajectory.TotalTime()) {
frc::Trajectory::State groundTruthState = trajectory.Sample(t);
if (lastVisionUpdateTime + kVisionUpdateRate < t) {
if (lastVisionPose != frc::Pose2d{}) {
estimator.AddVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
groundTruthState.pose +
frc::Transform2d{
frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.1 * 1_rad}};
visionPoses.push_back(lastVisionPose);
lastVisionUpdateTime = t;
// We are due for a new vision measurement if it's been `visionUpdateRate`
// seconds since the last vision measurement
if (visionPoses.empty() ||
visionPoses.back().first + kVisionUpdateRate < t) {
auto visionPose =
visionMeasurementGenerator(groundTruthState) +
frc::Transform2d{frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.05_rad}};
visionPoses.push_back({t, visionPose});
}
auto wheelSpeeds = kinematics.ToWheelSpeeds(
{groundTruthState.velocity, 0_mps,
groundTruthState.velocity * groundTruthState.curvature});
// We should apply the oldest vision measurement if it has been
// `visionUpdateDelay` seconds since it was measured
if (!visionPoses.empty() &&
visionPoses.front().first + kVisionUpdateDelay < t) {
auto visionEntry = visionPoses.front();
estimator.AddVisionMeasurement(visionEntry.second, visionEntry.first);
visionPoses.erase(visionPoses.begin());
visionLog.push_back({t, visionEntry.first, visionEntry.second});
}
auto chassisSpeeds = chassisSpeedsGenerator(groundTruthState);
auto wheelSpeeds = kinematics.ToWheelSpeeds(chassisSpeeds);
wheelPositions.frontLeft += wheelSpeeds.frontLeft * dt;
wheelPositions.frontRight += wheelSpeeds.frontRight * dt;
@@ -77,8 +81,18 @@ TEST(MecanumDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
auto xhat = estimator.UpdateWithTime(
t,
groundTruthState.pose.Rotation() +
frc::Rotation2d{distribution(generator) * 0.05_rad},
wheelSpeeds, wheelPositions);
frc::Rotation2d{distribution(generator) * 0.05_rad} -
trajectory.InitialPose().Rotation(),
wheelPositions);
if (debug) {
fmt::print("{}, {}, {}, {}, {}, {}, {}\n", t.value(), xhat.X().value(),
xhat.Y().value(), xhat.Rotation().Radians().value(),
groundTruthState.pose.X().value(),
groundTruthState.pose.Y().value(),
groundTruthState.pose.Rotation().Radians().value());
}
double error = groundTruthState.pose.Translation()
.Distance(xhat.Translation())
.value();
@@ -91,89 +105,104 @@ TEST(MecanumDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
t += dt;
}
EXPECT_LT(errorSum / (trajectory.TotalTime().value() / dt.value()), 0.05);
EXPECT_LT(maxError, 0.125);
if (debug) {
fmt::print("apply_time, measured_time, vision_x, vision_y, vision_theta\n");
units::second_t apply_time;
units::second_t measure_time;
frc::Pose2d vision_pose;
for (auto record : visionLog) {
std::tie(apply_time, measure_time, vision_pose) = record;
fmt::print("{}, {}, {}, {}, {}\n", apply_time.value(),
measure_time.value(), vision_pose.X().value(),
vision_pose.Y().value(),
vision_pose.Rotation().Radians().value());
}
}
EXPECT_NEAR(endingPose.X().value(),
estimator.GetEstimatedPosition().X().value(), 0.08);
EXPECT_NEAR(endingPose.Y().value(),
estimator.GetEstimatedPosition().Y().value(), 0.08);
EXPECT_NEAR(endingPose.Rotation().Radians().value(),
estimator.GetEstimatedPosition().Rotation().Radians().value(),
0.15);
if (checkError) {
EXPECT_LT(errorSum / (trajectory.TotalTime() / dt), 0.051);
EXPECT_LT(maxError, 0.2);
}
}
TEST(MecanumDrivePoseEstimatorTest, AccuracyFacingXAxis) {
TEST(MecanumDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
frc::MecanumDriveKinematics kinematics{
frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
frc::Translation2d{-1_m, -1_m}, frc::Translation2d{-1_m, 1_m}};
frc::MecanumDriveWheelPositions wheelPositions;
frc::MecanumDrivePoseEstimator estimator{frc::Rotation2d{},
wheelPositions,
frc::Pose2d{},
kinematics,
{0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1},
{0.05, 0.05, 0.05, 0.05, 0.05},
{0.1, 0.1, 0.1}};
frc::MecanumDrivePoseEstimator estimator{kinematics, frc::Rotation2d{},
wheelPositions, frc::Pose2d{},
{0.1, 0.1, 0.1}, {0.45, 0.45, 0.45}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(5.0_mps, 2.0_mps_sq));
frc::TrajectoryConfig(2.0_mps, 2.0_mps_sq));
std::default_random_engine generator;
std::normal_distribution<double> distribution(0.0, 1.0);
units::second_t dt = 0.02_s;
units::second_t t = 0_s;
units::second_t kVisionUpdateRate = 0.1_s;
frc::Pose2d lastVisionPose;
units::second_t lastVisionUpdateTime{-std::numeric_limits<double>::max()};
std::vector<frc::Pose2d> visionPoses;
double maxError = -std::numeric_limits<double>::max();
double errorSum = 0;
while (t < trajectory.TotalTime()) {
frc::Trajectory::State groundTruthState = trajectory.Sample(t);
if (lastVisionUpdateTime + kVisionUpdateRate < t) {
if (lastVisionPose != frc::Pose2d{}) {
estimator.AddVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
groundTruthState.pose +
frc::Transform2d{
frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.1 * 1_rad}};
visionPoses.push_back(lastVisionPose);
lastVisionUpdateTime = t;
}
auto wheelSpeeds = kinematics.ToWheelSpeeds(
{groundTruthState.velocity * groundTruthState.pose.Rotation().Cos(),
groundTruthState.velocity * groundTruthState.pose.Rotation().Sin(),
0_rad_per_s});
wheelPositions.frontLeft += wheelSpeeds.frontLeft * dt;
wheelPositions.frontRight += wheelSpeeds.frontRight * dt;
wheelPositions.rearLeft += wheelSpeeds.rearLeft * dt;
wheelPositions.rearRight += wheelSpeeds.rearRight * dt;
auto xhat = estimator.UpdateWithTime(
t, frc::Rotation2d{distribution(generator) * 0.05_rad}, wheelSpeeds,
wheelPositions);
double error = groundTruthState.pose.Translation()
.Distance(xhat.Translation())
.value();
if (error > maxError) {
maxError = error;
}
errorSum += error;
t += dt;
}
EXPECT_LT(errorSum / (trajectory.TotalTime().value() / dt.value()), 0.05);
EXPECT_LT(maxError, 0.125);
testFollowTrajectory(
kinematics, estimator, trajectory,
[&](frc::Trajectory::State& state) {
return frc::ChassisSpeeds{state.velocity, 0_mps,
state.velocity * state.curvature};
},
[&](frc::Trajectory::State& state) { return state.pose; },
trajectory.InitialPose(), {0_m, 0_m, frc::Rotation2d{45_deg}}, 0.02_s,
0.1_s, 0.25_s, true, false);
}
TEST(MecanumDrivePoseEstimatorTest, BadInitialPose) {
frc::MecanumDriveKinematics kinematics{
frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
frc::Translation2d{-1_m, -1_m}, frc::Translation2d{-1_m, 1_m}};
frc::MecanumDriveWheelPositions wheelPositions;
frc::MecanumDrivePoseEstimator estimator{kinematics, frc::Rotation2d{},
wheelPositions, frc::Pose2d{},
{0.1, 0.1, 0.1}, {0.45, 0.45, 0.1}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(2.0_mps, 2.0_mps_sq));
for (units::degree_t offset_direction_degs = 0_deg;
offset_direction_degs < 360_deg; offset_direction_degs += 45_deg) {
for (units::degree_t offset_heading_degs = 0_deg;
offset_heading_degs < 360_deg; offset_heading_degs += 45_deg) {
auto pose_offset = frc::Rotation2d{offset_direction_degs};
auto heading_offset = frc::Rotation2d{offset_heading_degs};
auto initial_pose =
trajectory.InitialPose() +
frc::Transform2d{frc::Translation2d{pose_offset.Cos() * 1_m,
pose_offset.Sin() * 1_m},
heading_offset};
testFollowTrajectory(
kinematics, estimator, trajectory,
[&](frc::Trajectory::State& state) {
return frc::ChassisSpeeds{state.velocity, 0_mps,
state.velocity * state.curvature};
},
[&](frc::Trajectory::State& state) { return state.pose; },
initial_pose, {0_m, 0_m, frc::Rotation2d{45_deg}}, 0.02_s, 0.1_s,
0.25_s, false, false);
}
}
}

306
wpimath/src/test/native/cpp/estimator/SwerveDrivePoseEstimatorTest.cpp
View File

@@ -3,7 +3,11 @@
// the WPILib BSD license file in the root directory of this project.
#include <limits>
#include <numbers>
#include <random>
#include <tuple>
#include <fmt/format.h>
#include "frc/estimator/SwerveDrivePoseEstimator.h"
#include "frc/geometry/Pose2d.h"
@@ -11,6 +15,128 @@
#include "frc/trajectory/TrajectoryGenerator.h"
#include "gtest/gtest.h"
void testFollowTrajectory(
const frc::SwerveDriveKinematics<4>& kinematics,
frc::SwerveDrivePoseEstimator<4>& estimator,
const frc::Trajectory& trajectory,
std::function<frc::ChassisSpeeds(frc::Trajectory::State&)>
chassisSpeedsGenerator,
std::function<frc::Pose2d(frc::Trajectory::State&)>
visionMeasurementGenerator,
const frc::Pose2d& startingPose, const frc::Pose2d& endingPose,
const units::second_t dt, const units::second_t kVisionUpdateRate,
const units::second_t kVisionUpdateDelay, const bool checkError,
const bool debug) {
wpi::array<frc::SwerveModulePosition, 4> positions{wpi::empty_array};
estimator.ResetPosition(frc::Rotation2d{}, positions, startingPose);
std::default_random_engine generator;
std::normal_distribution<double> distribution(0.0, 1.0);
units::second_t t = 0_s;
std::vector<std::pair<units::second_t, frc::Pose2d>> visionPoses;
std::vector<std::tuple<units::second_t, units::second_t, frc::Pose2d>>
visionLog;
double maxError = -std::numeric_limits<double>::max();
double errorSum = 0;
if (debug) {
fmt::print("time, est_x, est_y, est_theta, true_x, true_y, true_theta\n");
}
while (t < trajectory.TotalTime()) {
frc::Trajectory::State groundTruthState = trajectory.Sample(t);
// We are due for a new vision measurement if it's been `visionUpdateRate`
// seconds since the last vision measurement
if (visionPoses.empty() ||
visionPoses.back().first + kVisionUpdateRate < t) {
auto visionPose =
visionMeasurementGenerator(groundTruthState) +
frc::Transform2d{frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.05_rad}};
visionPoses.push_back({t, visionPose});
}
// We should apply the oldest vision measurement if it has been
// `visionUpdateDelay` seconds since it was measured
if (!visionPoses.empty() &&
visionPoses.front().first + kVisionUpdateDelay < t) {
auto visionEntry = visionPoses.front();
estimator.AddVisionMeasurement(visionEntry.second, visionEntry.first);
visionPoses.erase(visionPoses.begin());
visionLog.push_back({t, visionEntry.first, visionEntry.second});
}
auto chassisSpeeds = chassisSpeedsGenerator(groundTruthState);
auto moduleStates = kinematics.ToSwerveModuleStates(chassisSpeeds);
for (size_t i = 0; i < 4; i++) {
positions[i].distance += moduleStates[i].speed * dt;
positions[i].angle = moduleStates[i].angle;
}
auto xhat = estimator.UpdateWithTime(
t,
groundTruthState.pose.Rotation() +
frc::Rotation2d{distribution(generator) * 0.05_rad} -
trajectory.InitialPose().Rotation(),
positions);
if (debug) {
fmt::print("{}, {}, {}, {}, {}, {}, {}\n", t.value(), xhat.X().value(),
xhat.Y().value(), xhat.Rotation().Radians().value(),
groundTruthState.pose.X().value(),
groundTruthState.pose.Y().value(),
groundTruthState.pose.Rotation().Radians().value());
}
double error = groundTruthState.pose.Translation()
.Distance(xhat.Translation())
.value();
if (error > maxError) {
maxError = error;
}
errorSum += error;
t += dt;
}
if (debug) {
fmt::print("apply_time, measured_time, vision_x, vision_y, vision_theta\n");
units::second_t apply_time;
units::second_t measure_time;
frc::Pose2d vision_pose;
for (auto record : visionLog) {
std::tie(apply_time, measure_time, vision_pose) = record;
fmt::print("{}, {}, {}, {}, {}\n", apply_time.value(),
measure_time.value(), vision_pose.X().value(),
vision_pose.Y().value(),
vision_pose.Rotation().Radians().value());
}
}
EXPECT_NEAR(endingPose.X().value(),
estimator.GetEstimatedPosition().X().value(), 0.08);
EXPECT_NEAR(endingPose.Y().value(),
estimator.GetEstimatedPosition().Y().value(), 0.08);
EXPECT_NEAR(endingPose.Rotation().Radians().value(),
estimator.GetEstimatedPosition().Rotation().Radians().value(),
0.15);
if (checkError) {
EXPECT_LT(errorSum / (trajectory.TotalTime() / dt), 0.058);
EXPECT_LT(maxError, 0.2);
}
}
TEST(SwerveDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
frc::SwerveDriveKinematics<4> kinematics{
frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
@@ -22,88 +148,28 @@ TEST(SwerveDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
frc::SwerveModulePosition br;
frc::SwerveDrivePoseEstimator<4> estimator{
frc::Rotation2d{},
{fl, fr, bl, br},
frc::Pose2d{},
kinematics,
{0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1},
{0.05, 0.05, 0.05, 0.05, 0.05},
{0.1, 0.1, 0.1}};
kinematics, frc::Rotation2d{}, {fl, fr, bl, br},
frc::Pose2d{}, {0.1, 0.1, 0.1}, {0.45, 0.45, 0.45}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(5.0_mps, 2.0_mps_sq));
frc::TrajectoryConfig(2_mps, 2.0_mps_sq));
std::default_random_engine generator;
std::normal_distribution<double> distribution(0.0, 1.0);
units::second_t dt = 0.02_s;
units::second_t t = 0_s;
units::second_t kVisionUpdateRate = 0.1_s;
frc::Pose2d lastVisionPose;
units::second_t lastVisionUpdateTime{-std::numeric_limits<double>::max()};
std::vector<frc::Pose2d> visionPoses;
double maxError = -std::numeric_limits<double>::max();
double errorSum = 0;
while (t < trajectory.TotalTime()) {
frc::Trajectory::State groundTruthState = trajectory.Sample(t);
if (lastVisionUpdateTime + kVisionUpdateRate < t) {
if (lastVisionPose != frc::Pose2d{}) {
estimator.AddVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
groundTruthState.pose +
frc::Transform2d{frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.1_rad}};
visionPoses.push_back(lastVisionPose);
lastVisionUpdateTime = t;
}
auto moduleStates = kinematics.ToSwerveModuleStates(
{groundTruthState.velocity, 0_mps,
groundTruthState.velocity * groundTruthState.curvature});
fl.distance += moduleStates[0].speed * dt;
fr.distance += moduleStates[1].speed * dt;
bl.distance += moduleStates[2].speed * dt;
br.distance += moduleStates[3].speed * dt;
fl.angle = moduleStates[0].angle;
fr.angle = moduleStates[1].angle;
bl.angle = moduleStates[2].angle;
br.angle = moduleStates[3].angle;
auto xhat = estimator.UpdateWithTime(
t,
groundTruthState.pose.Rotation() +
frc::Rotation2d{distribution(generator) * 0.05_rad},
moduleStates, {fl, fr, bl, br});
double error = groundTruthState.pose.Translation()
.Distance(xhat.Translation())
.value();
if (error > maxError) {
maxError = error;
}
errorSum += error;
t += dt;
}
EXPECT_LT(errorSum / (trajectory.TotalTime().value() / dt.value()), 0.05);
EXPECT_LT(maxError, 0.125);
testFollowTrajectory(
kinematics, estimator, trajectory,
[&](frc::Trajectory::State& state) {
return frc::ChassisSpeeds{state.velocity, 0_mps,
state.velocity * state.curvature};
},
[&](frc::Trajectory::State& state) { return state.pose; },
{0_m, 0_m, frc::Rotation2d{45_deg}}, {0_m, 0_m, frc::Rotation2d{45_deg}},
0.02_s, 0.1_s, 0.25_s, true, false);
}
TEST(SwerveDrivePoseEstimatorTest, AccuracyFacingXAxis) {
TEST(SwerveDrivePoseEstimatorTest, BadInitialPose) {
frc::SwerveDriveKinematics<4> kinematics{
frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
frc::Translation2d{-1_m, -1_m}, frc::Translation2d{-1_m, 1_m}};
@@ -114,86 +180,38 @@ TEST(SwerveDrivePoseEstimatorTest, AccuracyFacingXAxis) {
frc::SwerveModulePosition br;
frc::SwerveDrivePoseEstimator<4> estimator{
frc::Rotation2d{},
{fl, fr, bl, br},
frc::Pose2d{},
kinematics,
{0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1},
{0.05, 0.05, 0.05, 0.05, 0.05},
{0.1, 0.1, 0.1}};
kinematics, frc::Rotation2d{}, {fl, fr, bl, br},
frc::Pose2d{}, {0.1, 0.1, 0.1}, {0.9, 0.9, 0.9}};
frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 135_deg},
frc::Pose2d{-3_m, 0_m, -90_deg},
frc::Pose2d{0_m, 0_m, 45_deg}},
frc::TrajectoryConfig(5.0_mps, 2.0_mps_sq));
frc::TrajectoryConfig(2_mps, 2.0_mps_sq));
std::default_random_engine generator;
std::normal_distribution<double> distribution(0.0, 1.0);
for (units::degree_t offset_direction_degs = 0_deg;
offset_direction_degs < 360_deg; offset_direction_degs += 45_deg) {
for (units::degree_t offset_heading_degs = 0_deg;
offset_heading_degs < 360_deg; offset_heading_degs += 45_deg) {
auto pose_offset = frc::Rotation2d{offset_direction_degs};
auto heading_offset = frc::Rotation2d{offset_heading_degs};
units::second_t dt = 0.02_s;
units::second_t t = 0_s;
auto initial_pose =
trajectory.InitialPose() +
frc::Transform2d{frc::Translation2d{pose_offset.Cos() * 1_m,
pose_offset.Sin() * 1_m},
heading_offset};
units::second_t kVisionUpdateRate = 0.1_s;
frc::Pose2d lastVisionPose;
units::second_t lastVisionUpdateTime{-std::numeric_limits<double>::max()};
std::vector<frc::Pose2d> visionPoses;
double maxError = -std::numeric_limits<double>::max();
double errorSum = 0;
while (t < trajectory.TotalTime()) {
frc::Trajectory::State groundTruthState = trajectory.Sample(t);
if (lastVisionUpdateTime + kVisionUpdateRate < t) {
if (lastVisionPose != frc::Pose2d{}) {
estimator.AddVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
}
lastVisionPose =
groundTruthState.pose +
frc::Transform2d{frc::Translation2d{distribution(generator) * 0.1_m,
distribution(generator) * 0.1_m},
frc::Rotation2d{distribution(generator) * 0.1_rad}};
visionPoses.push_back(lastVisionPose);
lastVisionUpdateTime = t;
testFollowTrajectory(
kinematics, estimator, trajectory,
[&](frc::Trajectory::State& state) {
return frc::ChassisSpeeds{state.velocity, 0_mps,
state.velocity * state.curvature};
},
[&](frc::Trajectory::State& state) { return state.pose; },
initial_pose, {0_m, 0_m, frc::Rotation2d{45_deg}}, 0.02_s, 0.1_s,
0.25_s, false, false);
}
auto moduleStates = kinematics.ToSwerveModuleStates(
{groundTruthState.velocity * groundTruthState.pose.Rotation().Cos(),
groundTruthState.velocity * groundTruthState.pose.Rotation().Sin(),
0_rad_per_s});
fl.distance += groundTruthState.velocity * dt +
0.5 * groundTruthState.acceleration * dt * dt;
fr.distance += groundTruthState.velocity * dt +
0.5 * groundTruthState.acceleration * dt * dt;
bl.distance += groundTruthState.velocity * dt +
0.5 * groundTruthState.acceleration * dt * dt;
br.distance += groundTruthState.velocity * dt +
0.5 * groundTruthState.acceleration * dt * dt;
fl.angle = groundTruthState.pose.Rotation();
fr.angle = groundTruthState.pose.Rotation();
bl.angle = groundTruthState.pose.Rotation();
br.angle = groundTruthState.pose.Rotation();
auto xhat = estimator.UpdateWithTime(
t, frc::Rotation2d{distribution(generator) * 0.05_rad}, moduleStates,
{fl, fr, bl, br});
double error = groundTruthState.pose.Translation()
.Distance(xhat.Translation())
.value();
if (error > maxError) {
maxError = error;
}
errorSum += error;
t += dt;
}
EXPECT_LT(errorSum / (trajectory.TotalTime().value() / dt.value()), 0.05);
EXPECT_LT(maxError, 0.125);
}