Added ported C++ version of ISAM2 Kitti example
Thomas Jespersen
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file IMUKittiExampleGPS
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* @brief Example of application of ISAM2 for GPS-aided navigation on the KITTI VISION BENCHMARK SUITE
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* @author Ported by Thomas Jespersen (thomasj@tkjelectronics.dk), TKJ Electronics
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*/
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// GTSAM related includes.
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#include <gtsam/navigation/CombinedImuFactor.h>
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#include <gtsam/navigation/GPSFactor.h>
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#include <gtsam/navigation/ImuFactor.h>
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#include <gtsam/slam/dataset.h>
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#include <gtsam/slam/BetweenFactor.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/nonlinear/ISAM2Params.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/inference/Symbol.h>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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using namespace gtsam;
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using namespace std;
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using symbol_shorthand::X; // Pose3 (x,y,z,r,p,y)
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using symbol_shorthand::V; // Vel (xdot,ydot,zdot)
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using symbol_shorthand::B; // Bias (ax,ay,az,gx,gy,gz)
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typedef struct {
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double Time;
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double dt;
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Vector3 Accelerometer;
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Vector3 Gyroscope; // omega
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} imuMeasurement_t;
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typedef struct {
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double Time;
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Vector3 Position; // x,y,z
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} gpsMeasurement_t;
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const string output_filename = "IMUKittyExampleGPSResults.csv";
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int main(int argc, char* argv[])
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{
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string line;
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// Read IMU metadata and compute relative sensor pose transforms
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// BodyPtx BodyPty BodyPtz BodyPrx BodyPry BodyPrz AccelerometerSigma GyroscopeSigma IntegrationSigma AccelerometerBiasSigma GyroscopeBiasSigma AverageDeltaT
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// 0 0 0 0 0 0 0.01 0.000175 0 0.000167 2.91e-006 0.0100395199348279
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string IMU_metadata_file = findExampleDataFile("KittiEquivBiasedImu_metadata.txt");
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ifstream IMU_metadata(IMU_metadata_file.c_str());
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printf("-- Reading sensor metadata\n");
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getline(IMU_metadata, line, '\n'); // ignore the first line
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double BodyPtx, BodyPty, BodyPtz, BodyPrx, BodyPry, BodyPrz, AccelerometerSigma, GyroscopeSigma, IntegrationSigma, AccelerometerBiasSigma, GyroscopeBiasSigma, AverageDeltaT;
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getline(IMU_metadata, line, '\n');
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sscanf(line.c_str(), "%lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf", &BodyPtx, &BodyPty, &BodyPtz, &BodyPrx, &BodyPry, &BodyPrz, &AccelerometerSigma, &GyroscopeSigma, &IntegrationSigma, &AccelerometerBiasSigma, &GyroscopeBiasSigma, &AverageDeltaT);
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printf("IMU metadata: %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\n", BodyPtx, BodyPty, BodyPtz, BodyPrx, BodyPry, BodyPrz, AccelerometerSigma, GyroscopeSigma, IntegrationSigma, AccelerometerBiasSigma, GyroscopeBiasSigma, AverageDeltaT);
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Vector6 BodyP = (Vector(6) << BodyPtx, BodyPty, BodyPtz, BodyPrx, BodyPry, BodyPrz).finished();
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auto body_T_imu = Pose3::Expmap(BodyP);
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if (!body_T_imu.equals(Pose3(), 1e-5)) {
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printf("Currently only support IMUinBody is identity, i.e. IMU and body frame are the same");
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exit(-1);
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}
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// Read IMU data
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// Time dt accelX accelY accelZ omegaX omegaY omegaZ
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// 46534.47837579 46534.47837579 1.7114864219577 0.1717911743144 9.80533438749 -0.0032006241515747 0.031231284764596 -0.0063569265706488
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vector<imuMeasurement_t> IMU_measurements;
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string IMU_data_file = findExampleDataFile("KittiEquivBiasedImu.txt");
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printf("-- Reading IMU measurements from file\n");
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{
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ifstream IMU_data(IMU_data_file.c_str());
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getline(IMU_data, line, '\n'); // ignore the first line
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double time = 0, dt = 0, acc_x = 0, acc_y = 0, acc_z = 0, gyro_x = 0, gyro_y = 0, gyro_z = 0;
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while (!IMU_data.eof()) {
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getline(IMU_data, line, '\n');
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sscanf(line.c_str(), "%lf %lf %lf %lf %lf %lf %lf %lf", &time, &dt, &acc_x, &acc_y, &acc_z, &gyro_x,
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&gyro_y, &gyro_z);
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imuMeasurement_t measurement;
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measurement.Time = time;
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measurement.dt = dt;
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measurement.Accelerometer = Vector3(acc_x, acc_y, acc_z);
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measurement.Gyroscope = Vector3(gyro_x, gyro_y, gyro_z);
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IMU_measurements.push_back(measurement);
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}
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}
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// Read GPS data
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// Time,X,Y,Z
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// 46534.478375790000428,-6.8269361350059405424,-11.868164241239471224,0.040306091310000624617
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vector<gpsMeasurement_t> GPS_measurements;
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string GPS_data_file = findExampleDataFile("KittiGps_converted.txt");
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printf("-- Reading GPS measurements from file\n");
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{
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ifstream GPS_data(GPS_data_file.c_str());
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getline(GPS_data, line, '\n'); // ignore the first line
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double time = 0, gps_x = 0, gps_y = 0, gps_z = 0;
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while (!GPS_data.eof()) {
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getline(GPS_data, line, '\n');
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sscanf(line.c_str(), "%lf,%lf,%lf,%lf", &time, &gps_x, &gps_y, &gps_z);
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gpsMeasurement_t measurement;
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measurement.Time = time;
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measurement.Position = Vector3(gps_x, gps_y, gps_z);
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GPS_measurements.push_back(measurement);
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}
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}
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// Configure different variables
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double tOffset = GPS_measurements[0].Time;
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size_t firstGPSPose = 1;
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size_t GPSskip = 10; // Skip this many GPS measurements each time
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double g = 9.8;
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auto w_coriolis = Vector3(); // zero vector
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// Configure noise models
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noiseModel::Diagonal::shared_ptr noiseModelGPS = noiseModel::Diagonal::Precisions((Vector(6) << Vector3::Constant(0), Vector3::Constant(1.0/0.07)).finished());
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// Set initial conditions for the estimated trajectory
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auto currentPoseGlobal = Pose3(Rot3(), GPS_measurements[firstGPSPose].Position); // initial pose is the reference frame (navigation frame)
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auto currentVelocityGlobal = Vector3(); // the vehicle is stationary at the beginning at position 0,0,0
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auto currentBias = imuBias::ConstantBias(); // init with zero bias
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noiseModel::Diagonal::shared_ptr sigma_init_x = noiseModel::Isotropic::Precisions((Vector(6) << Vector3::Constant(0), Vector3::Constant(1.0)).finished());
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noiseModel::Diagonal::shared_ptr sigma_init_v = noiseModel::Isotropic::Sigma(3, 1000.0);
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noiseModel::Diagonal::shared_ptr sigma_init_b = noiseModel::Isotropic::Sigmas((Vector(6) << Vector3::Constant(0.100), Vector3::Constant(5.00e-05)).finished());
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// Set IMU preintegration parameters
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Matrix33 measured_acc_cov = Matrix33::Identity(3,3) * pow(AccelerometerSigma,2);
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Matrix33 measured_omega_cov = Matrix33::Identity(3,3) * pow(GyroscopeSigma,2);
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Matrix33 integration_error_cov = Matrix33::Identity(3,3) * pow(IntegrationSigma,2); // error committed in integrating position from velocities
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boost::shared_ptr<PreintegratedImuMeasurements::Params> IMU_params = PreintegratedImuMeasurements::Params::MakeSharedU(g);
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IMU_params->accelerometerCovariance = measured_acc_cov; // acc white noise in continuous
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IMU_params->integrationCovariance = integration_error_cov; // integration uncertainty continuous
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IMU_params->gyroscopeCovariance = measured_omega_cov; // gyro white noise in continuous
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IMU_params->omegaCoriolis = w_coriolis;
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std::shared_ptr<PreintegratedImuMeasurements> currentSummarizedMeasurement = nullptr;
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// Set ISAM2 parameters and create ISAM2 solver object
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ISAM2Params isamParams;
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isamParams.factorization = ISAM2Params::CHOLESKY;
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isamParams.relinearizeSkip = 10;
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ISAM2 isam(isamParams);
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// Create the factor graph and values object that will store new factors and values to add to the incremental graph
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NonlinearFactorGraph newFactors;
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Values newValues; // values storing the initial estimates of new nodes in the factor graph
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/// Main loop:
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/// (1) we read the measurements
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/// (2) we create the corresponding factors in the graph
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/// (3) we solve the graph to obtain and optimal estimate of robot trajectory
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printf("-- Starting main loop: inference is performed at each time step, but we plot trajectory every 10 steps\n");
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size_t imuMeasurementIndex = 0;
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for (size_t gpsMeasurementIndex = firstGPSPose; gpsMeasurementIndex < GPS_measurements.size() - 1; gpsMeasurementIndex++) {
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// At each non=IMU measurement we initialize a new node in the graph
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auto currentPoseKey = X(gpsMeasurementIndex);
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auto currentVelKey = V(gpsMeasurementIndex);
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auto currentBiasKey = B(gpsMeasurementIndex);
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double t = GPS_measurements[gpsMeasurementIndex].Time;
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if (gpsMeasurementIndex == firstGPSPose) {
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// Create initial estimate and prior on initial pose, velocity, and biases
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newValues.insert(currentPoseKey, currentPoseGlobal);
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newValues.insert(currentVelKey, currentVelocityGlobal);
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newValues.insert(currentBiasKey, currentBias);
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newFactors.add(PriorFactor<Pose3>(currentPoseKey, currentPoseGlobal, sigma_init_x));
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newFactors.add(PriorFactor<Vector3>(currentVelKey, currentVelocityGlobal, sigma_init_v));
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newFactors.add(PriorFactor<imuBias::ConstantBias>(currentBiasKey, currentBias, sigma_init_b));
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} else {
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double t_previous = GPS_measurements[gpsMeasurementIndex-1].Time;
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// Summarize IMU data between the previous GPS measurement and now
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currentSummarizedMeasurement = std::make_shared<PreintegratedImuMeasurements>(IMU_params, currentBias);
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static size_t includedIMUmeasurementCount = 0;
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while (imuMeasurementIndex < IMU_measurements.size() && IMU_measurements[imuMeasurementIndex].Time <= t) {
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if (IMU_measurements[imuMeasurementIndex].Time >= t_previous) {
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currentSummarizedMeasurement->integrateMeasurement(IMU_measurements[imuMeasurementIndex].Accelerometer, IMU_measurements[imuMeasurementIndex].Gyroscope, IMU_measurements[imuMeasurementIndex].dt);
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includedIMUmeasurementCount++;
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}
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imuMeasurementIndex++;
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}
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// Create IMU factor
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auto previousPoseKey = X(gpsMeasurementIndex-1);
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auto previousVelKey = V(gpsMeasurementIndex-1);
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auto previousBiasKey = B(gpsMeasurementIndex-1);
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newFactors.add(ImuFactor(
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previousPoseKey, previousVelKey,
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currentPoseKey, currentVelKey,
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previousBiasKey, *currentSummarizedMeasurement));
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// Bias evolution as given in the IMU metadata
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noiseModel::Diagonal::shared_ptr sigma_between_b = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(sqrt(includedIMUmeasurementCount) * AccelerometerBiasSigma), Vector3::Constant(sqrt(includedIMUmeasurementCount) * GyroscopeBiasSigma)).finished());
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newFactors.add(BetweenFactor<imuBias::ConstantBias>(previousBiasKey, currentBiasKey, imuBias::ConstantBias(), sigma_between_b));
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// Create GPS factor
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auto GPSPose = Pose3(currentPoseGlobal.rotation(), GPS_measurements[gpsMeasurementIndex].Position);
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if ((gpsMeasurementIndex % GPSskip) == 0) {
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newFactors.add(PriorFactor<Pose3>(currentPoseKey, GPSPose, noiseModelGPS));
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newValues.insert(currentPoseKey, GPSPose);
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printf("################ POSE INCLUDED AT TIME %lf ################\n", t);
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GPSPose.translation().print();
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printf("\n\n");
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} else {
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newValues.insert(currentPoseKey, currentPoseGlobal);
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}
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// Add initial values for velocity and bias based on the previous estimates
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newValues.insert(currentVelKey, currentVelocityGlobal);
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newValues.insert(currentBiasKey, currentBias);
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// Update solver
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// =======================================================================
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// We accumulate 2*GPSskip GPS measurements before updating the solver at
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//first so that the heading becomes observable.
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if (gpsMeasurementIndex > (firstGPSPose + 2*GPSskip)) {
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printf("################ NEW FACTORS AT TIME %lf ################\n", t);
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newFactors.print();
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isam.update(newFactors, newValues);
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// Reset the newFactors and newValues list
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newFactors.resize(0);
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newValues.clear();
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// Extract the result/current estimates
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Values result = isam.calculateEstimate();
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currentPoseGlobal = result.at<Pose3>(currentPoseKey);
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currentVelocityGlobal = result.at<Vector3>(currentVelKey);
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currentBias = result.at<imuBias::ConstantBias>(currentBiasKey);
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printf("\n################ POSE AT TIME %lf ################\n", t);
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currentPoseGlobal.print();
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printf("\n\n");
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}
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}
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}
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// Save results to file
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printf("\nWriting results to file...\n");
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FILE* fp_out = fopen(output_filename.c_str(), "w+");
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fprintf(fp_out, "#time(s),x(m),y(m),z(m),qx,qy,qz,qw,gt_x(m),gt_y(m),gt_z(m)\n");
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Values result = isam.calculateEstimate();
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for (size_t gpsMeasurementIndex = firstGPSPose; gpsMeasurementIndex < GPS_measurements.size() - 1; gpsMeasurementIndex++) {
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auto poseKey = X(gpsMeasurementIndex);
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auto velKey = V(gpsMeasurementIndex);
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auto biasKey = B(gpsMeasurementIndex);
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auto pose = result.at<Pose3>(poseKey);
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auto velocity = result.at<Vector3>(velKey);
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auto bias = result.at<imuBias::ConstantBias>(biasKey);
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auto pose_quat = pose.rotation().toQuaternion();
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auto gps = GPS_measurements[gpsMeasurementIndex].Position;
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fprintf(fp_out, "%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n",
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GPS_measurements[gpsMeasurementIndex].Time,
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pose.x(), pose.y(), pose.z(),
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pose_quat.x(), pose_quat.y(), pose_quat.z(), pose_quat.w(),
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gps(0), gps(1), gps(2));
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}
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fclose(fp_out);
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}
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