[wpimath] Replace UKF implementation with square root form (#4168)

Co-authored-by: Tyler Veness <calcmogul@gmail.com>
This commit is contained in:
Connor Worley
2022-06-08 22:19:01 -07:00
committed by GitHub
parent 45b7fc445b
commit a99c11c14c
22 changed files with 494 additions and 297 deletions

View File

@@ -110,7 +110,7 @@ class DifferentialDrivePoseEstimatorTest {
t += dt;
}
assertEquals(0.0, errorSum / (traj.getTotalTimeSeconds() / dt), 0.035, "Incorrect mean error");
assertEquals(0.0, maxError, 0.055, "Incorrect max error");
assertEquals(0.0, errorSum / (traj.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.1, "Incorrect max error");
}
}

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@@ -111,7 +111,7 @@ class MecanumDrivePoseEstimatorTest {
}
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.25, "Incorrect mean error");
assertEquals(0.0, maxError, 0.42, "Incorrect max error");
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.1, "Incorrect max error");
}
}

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@@ -16,7 +16,7 @@ class MerweScaledSigmaPointsTest {
void testZeroMeanPoints() {
var merweScaledSigmaPoints = new MerweScaledSigmaPoints<>(Nat.N2());
var points =
merweScaledSigmaPoints.sigmaPoints(
merweScaledSigmaPoints.squareRootSigmaPoints(
VecBuilder.fill(0, 0), Matrix.mat(Nat.N2(), Nat.N2()).fill(1, 0, 0, 1));
assertTrue(
@@ -31,8 +31,8 @@ class MerweScaledSigmaPointsTest {
void testNonzeroMeanPoints() {
var merweScaledSigmaPoints = new MerweScaledSigmaPoints<>(Nat.N2());
var points =
merweScaledSigmaPoints.sigmaPoints(
VecBuilder.fill(1, 2), Matrix.mat(Nat.N2(), Nat.N2()).fill(1, 0, 0, 10));
merweScaledSigmaPoints.squareRootSigmaPoints(
VecBuilder.fill(1, 2), Matrix.mat(Nat.N2(), Nat.N2()).fill(1, 0, 0, Math.sqrt(10)));
assertTrue(
points.isEqual(

View File

@@ -110,7 +110,7 @@ class SwerveDrivePoseEstimatorTest {
}
assertEquals(
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.25, "Incorrect mean error");
assertEquals(0.0, maxError, 0.42, "Incorrect max error");
0.0, errorSum / (trajectory.getTotalTimeSeconds() / dt), 0.05, "Incorrect mean error");
assertEquals(0.0, maxError, 0.1, "Incorrect max error");
}
}

View File

@@ -16,8 +16,8 @@ import edu.wpi.first.math.geometry.Pose2d;
import edu.wpi.first.math.geometry.Rotation2d;
import edu.wpi.first.math.numbers.N1;
import edu.wpi.first.math.numbers.N2;
import edu.wpi.first.math.numbers.N4;
import edu.wpi.first.math.numbers.N6;
import edu.wpi.first.math.numbers.N3;
import edu.wpi.first.math.numbers.N5;
import edu.wpi.first.math.system.Discretization;
import edu.wpi.first.math.system.NumericalIntegration;
import edu.wpi.first.math.system.NumericalJacobian;
@@ -31,91 +31,111 @@ import org.junit.jupiter.api.Test;
class UnscentedKalmanFilterTest {
@SuppressWarnings({"LocalVariableName", "ParameterName"})
private static Matrix<N6, N1> getDynamics(Matrix<N6, N1> x, Matrix<N2, N1> u) {
private static Matrix<N5, N1> getDynamics(Matrix<N5, N1> x, Matrix<N2, N1> u) {
var motors = DCMotor.getCIM(2);
var gHigh = 7.08;
var rb = 0.8382 / 2.0;
var r = 0.0746125;
var m = 63.503;
var J = 5.6;
// var gLow = 15.32; // Low gear ratio
var gHigh = 7.08; // High gear ratio
var rb = 0.8382 / 2.0; // Robot radius
var r = 0.0746125; // Wheel radius
var m = 63.503; // Robot mass
var J = 5.6; // Robot moment of inertia
var C1 =
-Math.pow(gHigh, 2)
* motors.KtNMPerAmp
/ (motors.KvRadPerSecPerVolt * motors.rOhms * r * r);
var C2 = gHigh * motors.KtNMPerAmp / (motors.rOhms * r);
var k1 = 1.0 / m + Math.pow(rb, 2) / J;
var k2 = 1.0 / m - Math.pow(rb, 2) / J;
var c = x.get(2, 0);
var s = x.get(3, 0);
var vl = x.get(4, 0);
var vr = x.get(5, 0);
var vl = x.get(3, 0);
var vr = x.get(4, 0);
var Vl = u.get(0, 0);
var Vr = u.get(1, 0);
var k1 = 1.0 / m + rb * rb / J;
var k2 = 1.0 / m - rb * rb / J;
var xvel = (vl + vr) / 2;
var w = (vr - vl) / (2.0 * rb);
var v = 0.5 * (vl + vr);
return VecBuilder.fill(
xvel * c,
xvel * s,
-s * w,
c * w,
k1 * ((C1 * vl) + (C2 * Vl)) + k2 * ((C1 * vr) + (C2 * Vr)),
k2 * ((C1 * vl) + (C2 * Vl)) + k1 * ((C1 * vr) + (C2 * Vr)));
v * Math.cos(x.get(2, 0)),
v * Math.sin(x.get(2, 0)),
(vr - vl) / (2.0 * rb),
k1 * (C1 * vl + C2 * Vl) + k2 * (C1 * vr + C2 * Vr),
k2 * (C1 * vl + C2 * Vl) + k1 * (C1 * vr + C2 * Vr));
}
@SuppressWarnings({"PMD.UnusedFormalParameter", "ParameterName"})
private static Matrix<N4, N1> getLocalMeasurementModel(Matrix<N6, N1> x, Matrix<N2, N1> u) {
return VecBuilder.fill(x.get(2, 0), x.get(3, 0), x.get(4, 0), x.get(5, 0));
private static Matrix<N3, N1> getLocalMeasurementModel(Matrix<N5, N1> x, Matrix<N2, N1> u) {
return VecBuilder.fill(x.get(2, 0), x.get(3, 0), x.get(4, 0));
}
@SuppressWarnings({"PMD.UnusedFormalParameter", "ParameterName"})
private static Matrix<N6, N1> getGlobalMeasurementModel(Matrix<N6, N1> x, Matrix<N2, N1> u) {
private static Matrix<N5, N1> getGlobalMeasurementModel(Matrix<N5, N1> x, Matrix<N2, N1> u) {
return x.copy();
}
@Test
@SuppressWarnings("LocalVariableName")
void testInit() {
var dtSeconds = 0.005;
assertDoesNotThrow(
() -> {
UnscentedKalmanFilter<N6, N2, N4> observer =
UnscentedKalmanFilter<N5, N2, N3> observer =
new UnscentedKalmanFilter<>(
Nat.N6(),
Nat.N4(),
Nat.N5(),
Nat.N3(),
UnscentedKalmanFilterTest::getDynamics,
UnscentedKalmanFilterTest::getLocalMeasurementModel,
VecBuilder.fill(0.5, 0.5, 0.7, 0.7, 1.0, 1.0),
VecBuilder.fill(0.001, 0.001, 0.5, 0.5),
0.00505);
VecBuilder.fill(0.5, 0.5, 10.0, 1.0, 1.0),
VecBuilder.fill(0.0001, 0.01, 0.01),
AngleStatistics.angleMean(2),
AngleStatistics.angleMean(0),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(0),
AngleStatistics.angleAdd(2),
dtSeconds);
var u = VecBuilder.fill(12.0, 12.0);
observer.predict(u, 0.00505);
observer.predict(u, dtSeconds);
var localY = getLocalMeasurementModel(observer.getXhat(), u);
observer.correct(u, localY);
var globalY = getGlobalMeasurementModel(observer.getXhat(), u);
var R =
StateSpaceUtil.makeCovarianceMatrix(
Nat.N5(), VecBuilder.fill(0.01, 0.01, 0.0001, 0.01, 0.01));
observer.correct(
Nat.N5(),
u,
globalY,
UnscentedKalmanFilterTest::getGlobalMeasurementModel,
R,
AngleStatistics.angleMean(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleAdd(2));
});
}
@SuppressWarnings("LocalVariableName")
@Test
void testConvergence() {
double dtSeconds = 0.00505;
double dtSeconds = 0.005;
double rbMeters = 0.8382 / 2.0; // Robot radius
UnscentedKalmanFilter<N6, N2, N4> observer =
UnscentedKalmanFilter<N5, N2, N3> observer =
new UnscentedKalmanFilter<>(
Nat.N6(),
Nat.N4(),
Nat.N5(),
Nat.N3(),
UnscentedKalmanFilterTest::getDynamics,
UnscentedKalmanFilterTest::getLocalMeasurementModel,
VecBuilder.fill(0.5, 0.5, 0.7, 0.7, 1.0, 1.0),
VecBuilder.fill(0.001, 0.001, 0.5, 0.5),
VecBuilder.fill(0.5, 0.5, 10.0, 1.0, 1.0),
VecBuilder.fill(0.0001, 0.5, 0.5),
AngleStatistics.angleMean(2),
AngleStatistics.angleMean(0),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(0),
AngleStatistics.angleAdd(2),
dtSeconds);
List<Pose2d> waypoints =
@@ -125,56 +145,52 @@ class UnscentedKalmanFilterTest {
var trajectory =
TrajectoryGenerator.generateTrajectory(waypoints, new TrajectoryConfig(8.8, 0.1));
Matrix<N6, N1> nextR;
Matrix<N5, N1> r = new Matrix<>(Nat.N5(), Nat.N1());
Matrix<N2, N1> u = new Matrix<>(Nat.N2(), Nat.N1());
var B =
NumericalJacobian.numericalJacobianU(
Nat.N6(),
Nat.N5(),
Nat.N2(),
UnscentedKalmanFilterTest::getDynamics,
new Matrix<>(Nat.N6(), Nat.N1()),
u);
new Matrix<>(Nat.N5(), Nat.N1()),
new Matrix<>(Nat.N2(), Nat.N1()));
observer.setXhat(VecBuilder.fill(2.75, 22.521, 1.0, 0.0, 0.0, 0.0)); // TODO not hard code this
var ref = trajectory.sample(0.0);
Matrix<N6, N1> r =
observer.setXhat(
VecBuilder.fill(
ref.poseMeters.getTranslation().getX(),
ref.poseMeters.getTranslation().getY(),
ref.poseMeters.getRotation().getCos(),
ref.poseMeters.getRotation().getSin(),
ref.velocityMetersPerSecond * (1 - (ref.curvatureRadPerMeter * rbMeters)),
ref.velocityMetersPerSecond * (1 + (ref.curvatureRadPerMeter * rbMeters)));
nextR = r.copy();
trajectory.getInitialPose().getTranslation().getX(),
trajectory.getInitialPose().getTranslation().getY(),
trajectory.getInitialPose().getRotation().getRadians(),
0.0,
0.0));
var trueXhat = observer.getXhat();
double totalTime = trajectory.getTotalTimeSeconds();
for (int i = 0; i < (totalTime / dtSeconds); i++) {
ref = trajectory.sample(dtSeconds * i);
var ref = trajectory.sample(dtSeconds * i);
double vl = ref.velocityMetersPerSecond * (1 - (ref.curvatureRadPerMeter * rbMeters));
double vr = ref.velocityMetersPerSecond * (1 + (ref.curvatureRadPerMeter * rbMeters));
nextR.set(0, 0, ref.poseMeters.getTranslation().getX());
nextR.set(1, 0, ref.poseMeters.getTranslation().getY());
nextR.set(2, 0, ref.poseMeters.getRotation().getCos());
nextR.set(3, 0, ref.poseMeters.getRotation().getSin());
nextR.set(4, 0, vl);
nextR.set(5, 0, vr);
var nextR =
VecBuilder.fill(
ref.poseMeters.getTranslation().getX(),
ref.poseMeters.getTranslation().getY(),
ref.poseMeters.getRotation().getRadians(),
vl,
vr);
Matrix<N4, N1> localY = getLocalMeasurementModel(trueXhat, new Matrix<>(Nat.N2(), Nat.N1()));
var noiseStdDev = VecBuilder.fill(0.001, 0.001, 0.5, 0.5);
Matrix<N3, N1> localY = getLocalMeasurementModel(trueXhat, new Matrix<>(Nat.N2(), Nat.N1()));
var noiseStdDev = VecBuilder.fill(0.0001, 0.5, 0.5);
observer.correct(u, localY.plus(StateSpaceUtil.makeWhiteNoiseVector(noiseStdDev)));
var rdot = nextR.minus(r).div(dtSeconds);
u = new Matrix<>(B.solve(rdot.minus(getDynamics(r, new Matrix<>(Nat.N2(), Nat.N1())))));
r = nextR;
observer.predict(u, dtSeconds);
r = nextR;
trueXhat =
NumericalIntegration.rk4(UnscentedKalmanFilterTest::getDynamics, trueXhat, u, dtSeconds);
}
@@ -183,25 +199,28 @@ class UnscentedKalmanFilterTest {
observer.correct(u, localY);
var globalY = getGlobalMeasurementModel(trueXhat, u);
var R = StateSpaceUtil.makeCostMatrix(VecBuilder.fill(0.01, 0.01, 0.0001, 0.0001, 0.5, 0.5));
var R =
StateSpaceUtil.makeCovarianceMatrix(
Nat.N5(), VecBuilder.fill(0.01, 0.01, 0.0001, 0.5, 0.5));
observer.correct(
Nat.N6(),
Nat.N5(),
u,
globalY,
UnscentedKalmanFilterTest::getGlobalMeasurementModel,
R,
(sigmas, weights) -> sigmas.times(Matrix.changeBoundsUnchecked(weights)),
Matrix::minus,
Matrix::minus,
Matrix::plus);
AngleStatistics.angleMean(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleResidual(2),
AngleStatistics.angleAdd(2));
final var finalPosition = trajectory.sample(trajectory.getTotalTimeSeconds());
assertEquals(finalPosition.poseMeters.getTranslation().getX(), observer.getXhat(0), 0.25);
assertEquals(finalPosition.poseMeters.getTranslation().getY(), observer.getXhat(1), 0.25);
assertEquals(finalPosition.poseMeters.getRotation().getRadians(), observer.getXhat(2), 1.0);
assertEquals(0.0, observer.getXhat(3), 1.0);
assertEquals(0.0, observer.getXhat(4), 1.0);
assertEquals(finalPosition.poseMeters.getTranslation().getX(), observer.getXhat(0), 0.055);
assertEquals(finalPosition.poseMeters.getTranslation().getY(), observer.getXhat(1), 0.15);
assertEquals(
finalPosition.poseMeters.getRotation().getRadians(), observer.getXhat(2), 0.000005);
assertEquals(0.0, observer.getXhat(3), 0.1);
assertEquals(0.0, observer.getXhat(4), 0.1);
}
@Test
@@ -235,95 +254,24 @@ class UnscentedKalmanFilterTest {
assertEquals(ref.get(0, 0), observer.getXhat(0), 5);
}
@SuppressWarnings("LocalVariableName")
@Test
void testUnscentedTransform() {
// From FilterPy
var ret =
UnscentedKalmanFilter.unscentedTransform(
Nat.N4(),
Nat.N4(),
Matrix.mat(Nat.N4(), Nat.N9())
.fill(
-0.9,
-0.822540333075852,
-0.8922540333075852,
-0.9,
-0.9,
-0.9774596669241481,
-0.9077459666924148,
-0.9,
-0.9,
1.0,
1.0,
1.077459666924148,
1.0,
1.0,
1.0,
0.9225403330758519,
1.0,
1.0,
-0.9,
-0.9,
-0.9,
-0.822540333075852,
-0.8922540333075852,
-0.9,
-0.9,
-0.9774596669241481,
-0.9077459666924148,
1.0,
1.0,
1.0,
1.0,
1.077459666924148,
1.0,
1.0,
1.0,
0.9225403330758519),
VecBuilder.fill(
-132.33333333,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667),
VecBuilder.fill(
-129.34333333,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667,
16.66666667),
(sigmas, weights) -> sigmas.times(Matrix.changeBoundsUnchecked(weights)),
Matrix::minus);
void testRoundTripP() {
var dtSeconds = 0.005;
assertTrue(VecBuilder.fill(-0.9, 1, -0.9, 1).isEqual(ret.getFirst(), 1E-5));
var observer =
new UnscentedKalmanFilter<>(
Nat.N2(),
Nat.N2(),
(x, u) -> x,
(x, u) -> x,
VecBuilder.fill(0.0, 0.0),
VecBuilder.fill(0.0, 0.0),
dtSeconds);
assertTrue(
Matrix.mat(Nat.N4(), Nat.N4())
.fill(
2.02000002e-01,
2.00000500e-02,
-2.69044710e-29,
-4.59511477e-29,
2.00000500e-02,
2.00001000e-01,
-2.98781068e-29,
-5.12759588e-29,
-2.73372625e-29,
-3.09882635e-29,
2.02000002e-01,
2.00000500e-02,
-4.67065917e-29,
-5.10705197e-29,
2.00000500e-02,
2.00001000e-01)
.isEqual(ret.getSecond(), 1E-5));
var P = Matrix.mat(Nat.N2(), Nat.N2()).fill(2.0, 1.0, 1.0, 2.0);
observer.setP(P);
assertTrue(observer.getP().isEqual(P, 1e-9));
}
}