156 lines
5.6 KiB
C++
156 lines
5.6 KiB
C++
/**
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* @file testImuPreintegration.cpp
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* @brief Unit tests for IMU Preintegration
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* @author Russell Buchanan
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**/
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#include <tests/ImuMeasurement.h>
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#include <fstream>
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#include <iostream>
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/navigation/CombinedImuFactor.h>
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namespace drs {
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Measurement::Measurement() : dt(0), time(0), type("UNDEFINED") {}
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Measurement::Measurement(std::string _type) : dt(0), time(0), type(_type) {}
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ImuMeasurement::ImuMeasurement() : I_a_WI{0, 0, 0}, I_w_WI{0, 0, 0} { type = "ImuMeasurement"; }
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std::ostream& operator<<(std::ostream& stream, const ImuMeasurement& meas) {
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stream << "IMU Measurement at time = " << meas.time << " : \n"
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<< "dt : " << meas.dt << "\n"
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<< "I_a_WI: " << meas.I_a_WI.transpose() << "\n"
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<< "I_w_WI: " << meas.I_w_WI.transpose() << "\n";
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return stream;
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}
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} // namespace drs
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using namespace gtsam;
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/* ************************************************************************* */
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/// \brief Uses the GTSAM library to perform IMU preintegration on an acceleration input.
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///
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TEST(GtsamLibraryTests, LoadedSimulationData) {
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Eigen::Vector3d finalPos;
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std::vector<drs::ImuMeasurement> imuMeasurements;
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double accNoiseSigma = 0.001249;
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double accBiasRwSigma = 0.000106;
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double gyrNoiseSigma = 0.000208;
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double gyrBiasRwSigma = 0.000004;
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double integrationCovariance = 1e-8;
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double biasAccOmegaInt = 1e-5;
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double gravity = 9.81;
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double rate = 400.0; // Hz
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/// @todo Update directory to correct location
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std::string inFileString = "/home/russell/imu_data.csv";
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std::ofstream outputFile;
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outputFile.open("/home/russell/gtsam_output.csv", std::ofstream::out);
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std::ifstream inputFile(inFileString);
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std::string line;
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while (std::getline(inputFile, line)) {
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std::stringstream ss(line);
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std::string str;
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std::vector<double> results;
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while (getline(ss, str, ',')) {
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results.push_back(std::atof(str.c_str()));
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}
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drs::ImuMeasurement measurement;
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measurement.dt = static_cast<uint64_t>(1e9 * (1 / rate));
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measurement.time = results[2];
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measurement.I_a_WI = {results[29], results[30], results[31]};
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measurement.I_w_WI = {results[17], results[18], results[19]};
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imuMeasurements.push_back(measurement);
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// std::cout << "IMU measurement " << measurement << std::endl;
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}
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// Assume a Z-up navigation (assuming we are performing optimization in the IMU frame).
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boost::shared_ptr<gtsam::PreintegratedCombinedMeasurements::Params> imuPreintegratedParams =
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gtsam::PreintegratedCombinedMeasurements::Params::MakeSharedU(gravity);
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imuPreintegratedParams->accelerometerCovariance = I_3x3 * pow(accNoiseSigma, 2);
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imuPreintegratedParams->biasAccCovariance = I_3x3 * pow(accBiasRwSigma, 2);
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imuPreintegratedParams->gyroscopeCovariance = I_3x3 * pow(gyrNoiseSigma, 2);
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imuPreintegratedParams->biasOmegaCovariance = I_3x3 * pow(gyrBiasRwSigma, 2);
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imuPreintegratedParams->integrationCovariance = I_3x3 * integrationCovariance;
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imuPreintegratedParams->biasAccOmegaInt = I_6x6 * biasAccOmegaInt;
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// Initial state
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gtsam::Pose3 priorPose;
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gtsam::Vector3 priorVelocity;
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gtsam::imuBias::ConstantBias priorImuBias;
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gtsam::PreintegratedCombinedMeasurements imuPreintegrated;
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Eigen::Vector3d position;
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Eigen::Vector3d velocity;
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gtsam::NavState propState;
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gtsam::NavState initialNavState(priorPose, priorVelocity);
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// Bias estimated by my Algorithm
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priorImuBias =
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gtsam::imuBias::ConstantBias(Eigen::Vector3d(-0.0314648, 0.0219921, 6.95945e-05),
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Eigen::Vector3d(4.88581e-08, -1.04971e-09, -0.000122868));
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// zero bias
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// priorImuBias = gtsam::imuBias::ConstantBias(Eigen::Vector3d(0,0,0),
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// Eigen::Vector3d(0,0,0));
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imuPreintegrated = gtsam::PreintegratedCombinedMeasurements(imuPreintegratedParams, priorImuBias);
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// Put header row in output csv
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outputFile << "X Position,"
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<< "Y Position,"
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<< "Z Position,"
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<< "X Velocity,"
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<< "Y Velocity,"
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<< "Z Velocity,"
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<< "\n";
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for (int n = 1; n < imuMeasurements.size(); n++) { //start at 1 to skip header
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// integrate
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imuPreintegrated.integrateMeasurement(imuMeasurements[n].I_a_WI, imuMeasurements[n].I_w_WI,
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1 / rate);
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// predict
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propState = imuPreintegrated.predict(initialNavState, priorImuBias);
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position = propState.pose().translation();
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velocity = propState.velocity();
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// std::cout << "IMU Position " << position.transpose() << std::endl;
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// std::cout << "IMU Velocity " << velocity.transpose() << std::endl;
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// Write to csv
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outputFile << std::to_string(position.x()) << "," << std::to_string(position.y()) << ","
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<< std::to_string(position.z()) << "," << std::to_string(velocity.x()) << ","
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<< std::to_string(velocity.y()) << "," << std::to_string(velocity.z()) << ","
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<< "\n";
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}
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outputFile.close();
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gtsam::Vector3 rotation = propState.pose().rotation().rpy();
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// Dont have ground truth for x and y position yet
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// DOUBLES_EQUAL(0.1, position[0], 1e-2);
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// DOUBLES_EQUAL(0.1, position[1], 1e-2);
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DOUBLES_EQUAL(0.0, position[2], 1e-2);
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DOUBLES_EQUAL(0.0, rotation[0], 1e-2);
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DOUBLES_EQUAL(0.0, rotation[1], 1e-2);
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DOUBLES_EQUAL(0.0, rotation[2], 1e-2);
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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