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e22d8cc343
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>
218 lines
8.1 KiB
C++
218 lines
8.1 KiB
C++
// Copyright (c) FIRST and other WPILib contributors.
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// Open Source Software; you can modify and/or share it under the terms of
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// the WPILib BSD license file in the root directory of this project.
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#include <limits>
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#include <numbers>
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#include <random>
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#include <tuple>
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#include <fmt/format.h>
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#include "frc/estimator/SwerveDrivePoseEstimator.h"
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#include "frc/geometry/Pose2d.h"
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#include "frc/kinematics/SwerveDriveKinematics.h"
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#include "frc/trajectory/TrajectoryGenerator.h"
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#include "gtest/gtest.h"
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void testFollowTrajectory(
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const frc::SwerveDriveKinematics<4>& kinematics,
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frc::SwerveDrivePoseEstimator<4>& estimator,
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const frc::Trajectory& trajectory,
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std::function<frc::ChassisSpeeds(frc::Trajectory::State&)>
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chassisSpeedsGenerator,
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std::function<frc::Pose2d(frc::Trajectory::State&)>
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visionMeasurementGenerator,
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const frc::Pose2d& startingPose, const frc::Pose2d& endingPose,
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const units::second_t dt, const units::second_t kVisionUpdateRate,
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const units::second_t kVisionUpdateDelay, const bool checkError,
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const bool debug) {
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wpi::array<frc::SwerveModulePosition, 4> positions{wpi::empty_array};
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estimator.ResetPosition(frc::Rotation2d{}, positions, startingPose);
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std::default_random_engine generator;
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std::normal_distribution<double> distribution(0.0, 1.0);
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units::second_t t = 0_s;
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std::vector<std::pair<units::second_t, frc::Pose2d>> visionPoses;
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std::vector<std::tuple<units::second_t, units::second_t, frc::Pose2d>>
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visionLog;
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double maxError = -std::numeric_limits<double>::max();
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double errorSum = 0;
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if (debug) {
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fmt::print("time, est_x, est_y, est_theta, true_x, true_y, true_theta\n");
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}
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while (t < trajectory.TotalTime()) {
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frc::Trajectory::State groundTruthState = trajectory.Sample(t);
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// We are due for a new vision measurement if it's been `visionUpdateRate`
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// seconds since the last vision measurement
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if (visionPoses.empty() ||
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visionPoses.back().first + kVisionUpdateRate < t) {
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auto visionPose =
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visionMeasurementGenerator(groundTruthState) +
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frc::Transform2d{frc::Translation2d{distribution(generator) * 0.1_m,
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distribution(generator) * 0.1_m},
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frc::Rotation2d{distribution(generator) * 0.05_rad}};
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visionPoses.push_back({t, visionPose});
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}
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// We should apply the oldest vision measurement if it has been
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// `visionUpdateDelay` seconds since it was measured
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if (!visionPoses.empty() &&
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visionPoses.front().first + kVisionUpdateDelay < t) {
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auto visionEntry = visionPoses.front();
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estimator.AddVisionMeasurement(visionEntry.second, visionEntry.first);
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visionPoses.erase(visionPoses.begin());
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visionLog.push_back({t, visionEntry.first, visionEntry.second});
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}
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auto chassisSpeeds = chassisSpeedsGenerator(groundTruthState);
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auto moduleStates = kinematics.ToSwerveModuleStates(chassisSpeeds);
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for (size_t i = 0; i < 4; i++) {
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positions[i].distance += moduleStates[i].speed * dt;
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positions[i].angle = moduleStates[i].angle;
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}
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auto xhat = estimator.UpdateWithTime(
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t,
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groundTruthState.pose.Rotation() +
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frc::Rotation2d{distribution(generator) * 0.05_rad} -
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trajectory.InitialPose().Rotation(),
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positions);
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if (debug) {
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fmt::print("{}, {}, {}, {}, {}, {}, {}\n", t.value(), xhat.X().value(),
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xhat.Y().value(), xhat.Rotation().Radians().value(),
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groundTruthState.pose.X().value(),
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groundTruthState.pose.Y().value(),
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groundTruthState.pose.Rotation().Radians().value());
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}
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double error = groundTruthState.pose.Translation()
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.Distance(xhat.Translation())
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.value();
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if (error > maxError) {
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maxError = error;
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}
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errorSum += error;
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t += dt;
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}
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if (debug) {
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fmt::print("apply_time, measured_time, vision_x, vision_y, vision_theta\n");
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units::second_t apply_time;
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units::second_t measure_time;
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frc::Pose2d vision_pose;
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for (auto record : visionLog) {
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std::tie(apply_time, measure_time, vision_pose) = record;
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fmt::print("{}, {}, {}, {}, {}\n", apply_time.value(),
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measure_time.value(), vision_pose.X().value(),
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vision_pose.Y().value(),
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vision_pose.Rotation().Radians().value());
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}
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}
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EXPECT_NEAR(endingPose.X().value(),
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estimator.GetEstimatedPosition().X().value(), 0.08);
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EXPECT_NEAR(endingPose.Y().value(),
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estimator.GetEstimatedPosition().Y().value(), 0.08);
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EXPECT_NEAR(endingPose.Rotation().Radians().value(),
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estimator.GetEstimatedPosition().Rotation().Radians().value(),
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0.15);
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if (checkError) {
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EXPECT_LT(errorSum / (trajectory.TotalTime() / dt), 0.058);
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EXPECT_LT(maxError, 0.2);
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}
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}
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TEST(SwerveDrivePoseEstimatorTest, AccuracyFacingTrajectory) {
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frc::SwerveDriveKinematics<4> kinematics{
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frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
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frc::Translation2d{-1_m, -1_m}, frc::Translation2d{-1_m, 1_m}};
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frc::SwerveModulePosition fl;
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frc::SwerveModulePosition fr;
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frc::SwerveModulePosition bl;
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frc::SwerveModulePosition br;
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frc::SwerveDrivePoseEstimator<4> estimator{
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kinematics, frc::Rotation2d{}, {fl, fr, bl, br},
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frc::Pose2d{}, {0.1, 0.1, 0.1}, {0.45, 0.45, 0.45}};
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frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
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std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
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frc::Pose2d{0_m, 0_m, 135_deg},
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frc::Pose2d{-3_m, 0_m, -90_deg},
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frc::Pose2d{0_m, 0_m, 45_deg}},
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frc::TrajectoryConfig(2_mps, 2.0_mps_sq));
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testFollowTrajectory(
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kinematics, estimator, trajectory,
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[&](frc::Trajectory::State& state) {
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return frc::ChassisSpeeds{state.velocity, 0_mps,
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state.velocity * state.curvature};
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},
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[&](frc::Trajectory::State& state) { return state.pose; },
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{0_m, 0_m, frc::Rotation2d{45_deg}}, {0_m, 0_m, frc::Rotation2d{45_deg}},
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0.02_s, 0.1_s, 0.25_s, true, false);
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}
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TEST(SwerveDrivePoseEstimatorTest, BadInitialPose) {
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frc::SwerveDriveKinematics<4> kinematics{
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frc::Translation2d{1_m, 1_m}, frc::Translation2d{1_m, -1_m},
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frc::Translation2d{-1_m, -1_m}, frc::Translation2d{-1_m, 1_m}};
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frc::SwerveModulePosition fl;
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frc::SwerveModulePosition fr;
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frc::SwerveModulePosition bl;
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frc::SwerveModulePosition br;
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frc::SwerveDrivePoseEstimator<4> estimator{
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kinematics, frc::Rotation2d{}, {fl, fr, bl, br},
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frc::Pose2d{}, {0.1, 0.1, 0.1}, {0.9, 0.9, 0.9}};
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frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
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std::vector{frc::Pose2d{0_m, 0_m, 45_deg}, frc::Pose2d{3_m, 0_m, -90_deg},
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frc::Pose2d{0_m, 0_m, 135_deg},
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frc::Pose2d{-3_m, 0_m, -90_deg},
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frc::Pose2d{0_m, 0_m, 45_deg}},
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frc::TrajectoryConfig(2_mps, 2.0_mps_sq));
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for (units::degree_t offset_direction_degs = 0_deg;
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offset_direction_degs < 360_deg; offset_direction_degs += 45_deg) {
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for (units::degree_t offset_heading_degs = 0_deg;
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offset_heading_degs < 360_deg; offset_heading_degs += 45_deg) {
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auto pose_offset = frc::Rotation2d{offset_direction_degs};
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auto heading_offset = frc::Rotation2d{offset_heading_degs};
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auto initial_pose =
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trajectory.InitialPose() +
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frc::Transform2d{frc::Translation2d{pose_offset.Cos() * 1_m,
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pose_offset.Sin() * 1_m},
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heading_offset};
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testFollowTrajectory(
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kinematics, estimator, trajectory,
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[&](frc::Trajectory::State& state) {
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return frc::ChassisSpeeds{state.velocity, 0_mps,
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state.velocity * state.curvature};
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},
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[&](frc::Trajectory::State& state) { return state.pose; },
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initial_pose, {0_m, 0_m, frc::Rotation2d{45_deg}}, 0.02_s, 0.1_s,
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0.25_s, false, false);
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}
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}
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}
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