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Pose and state estimators can filter latency-compensated global measurements and fuse them with state-space drivetrain model information to estimate robot position. They are drop-in replacements for the existing odometry classes. Co-authored-by: Declan Freeman-Gleason <declanfreemangleason@gmail.com> Co-authored-by: Prateek Machiraju <prateek.machiraju@gmail.com> Co-authored-by: Claudius Tewari <cttewari@gmail.com> Co-authored-by: Matt <matthew.morley.ca@gmail.com>
94 lines
3.4 KiB
C++
94 lines
3.4 KiB
C++
/*----------------------------------------------------------------------------*/
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/* Copyright (c) 2020 FIRST. All Rights Reserved. */
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/* Open Source Software - may be modified and shared by FRC teams. The code */
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/* must be accompanied by the FIRST BSD license file in the root directory of */
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/* the project. */
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/*----------------------------------------------------------------------------*/
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#include <limits>
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#include <random>
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#include "frc/estimator/MecanumDrivePoseEstimator.h"
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#include "frc/geometry/Pose2d.h"
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#include "frc/kinematics/MecanumDriveKinematics.h"
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#include "frc/kinematics/MecanumDriveOdometry.h"
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#include "frc/trajectory/TrajectoryGenerator.h"
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#include "gtest/gtest.h"
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TEST(MecanumDrivePoseEstimatorTest, TestAccuracy) {
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frc::MecanumDriveKinematics 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::MecanumDrivePoseEstimator estimator{
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frc::Rotation2d(), frc::Pose2d(), kinematics,
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{0.1, 0.1, 0.1}, {0.05}, {0.1, 0.1, 0.1}};
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frc::MecanumDriveOdometry odometry{kinematics, frc::Rotation2d()};
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frc::Trajectory trajectory = frc::TrajectoryGenerator::GenerateTrajectory(
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std::vector{frc::Pose2d(0_m, 0_m, frc::Rotation2d(45_deg)),
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frc::Pose2d(3_m, 0_m, frc::Rotation2d(-90_deg)),
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frc::Pose2d(0_m, 0_m, frc::Rotation2d(135_deg)),
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frc::Pose2d(-3_m, 0_m, frc::Rotation2d(-90_deg)),
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frc::Pose2d(0_m, 0_m, frc::Rotation2d(45_deg))},
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frc::TrajectoryConfig(5.0_mps, 2.0_mps_sq));
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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 dt = 0.02_s;
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units::second_t t = 0_s;
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units::second_t kVisionUpdateRate = 0.1_s;
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frc::Pose2d lastVisionPose;
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units::second_t lastVisionUpdateTime{-std::numeric_limits<double>::max()};
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std::vector<frc::Pose2d> visionPoses;
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double maxError = -std::numeric_limits<double>::max();
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double errorSum = 0;
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while (t < trajectory.TotalTime()) {
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frc::Trajectory::State groundTruthState = trajectory.Sample(t);
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if (lastVisionUpdateTime + kVisionUpdateRate < t) {
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if (lastVisionPose != frc::Pose2d()) {
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estimator.AddVisionMeasurement(lastVisionPose, lastVisionUpdateTime);
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}
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lastVisionPose =
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groundTruthState.pose +
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frc::Transform2d(
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frc::Translation2d(distribution(generator) * 0.1_m,
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distribution(generator) * 0.1_m),
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frc::Rotation2d(distribution(generator) * 0.1 * 1_rad));
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visionPoses.push_back(lastVisionPose);
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lastVisionUpdateTime = t;
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}
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auto wheelSpeeds = kinematics.ToWheelSpeeds(
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{groundTruthState.velocity, 0_mps,
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groundTruthState.velocity * groundTruthState.curvature});
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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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wheelSpeeds);
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double error = groundTruthState.pose.Translation()
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.Distance(xhat.Translation())
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.to<double>();
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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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EXPECT_LT(errorSum / (trajectory.TotalTime().to<double>() / dt.to<double>()),
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0.2);
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EXPECT_LT(maxError, 0.4);
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}
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