385 lines
14 KiB
C++
385 lines
14 KiB
C++
/* ----------------------------------------------------------------------------
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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
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* VISION BENCHMARK SUITE
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* @author Ported by Thomas Jespersen (thomasj@tkjelectronics.dk), TKJ
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* Electronics
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*/
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// GTSAM related includes.
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#include <gtsam/inference/Symbol.h>
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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/nonlinear/ISAM2.h>
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#include <gtsam/nonlinear/ISAM2Params.h>
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#include <gtsam/nonlinear/NonlinearFactorGraph.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/slam/dataset.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 std;
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using namespace gtsam;
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using symbol_shorthand::B; // Bias (ax,ay,az,gx,gy,gz)
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using symbol_shorthand::V; // Vel (xdot,ydot,zdot)
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using symbol_shorthand::X; // Pose3 (x,y,z,r,p,y)
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struct KittiCalibration {
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double body_ptx;
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double body_pty;
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double body_ptz;
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double body_prx;
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double body_pry;
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double body_prz;
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double accelerometer_sigma;
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double gyroscope_sigma;
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double integration_sigma;
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double accelerometer_bias_sigma;
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double gyroscope_bias_sigma;
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double average_delta_t;
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};
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struct ImuMeasurement {
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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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};
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struct GpsMeasurement {
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double time;
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Vector3 position; // x,y,z
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};
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const string output_filename = "IMUKittiExampleGPSResults.csv";
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void loadKittiData(KittiCalibration& kitti_calibration,
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vector<ImuMeasurement>& imu_measurements,
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vector<GpsMeasurement>& gps_measurements) {
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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
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// GyroscopeSigma IntegrationSigma AccelerometerBiasSigma GyroscopeBiasSigma
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// AverageDeltaT
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string imu_metadata_file =
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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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// Load Kitti calibration
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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",
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&kitti_calibration.body_ptx, &kitti_calibration.body_pty,
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&kitti_calibration.body_ptz, &kitti_calibration.body_prx,
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&kitti_calibration.body_pry, &kitti_calibration.body_prz,
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&kitti_calibration.accelerometer_sigma,
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&kitti_calibration.gyroscope_sigma,
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&kitti_calibration.integration_sigma,
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&kitti_calibration.accelerometer_bias_sigma,
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&kitti_calibration.gyroscope_bias_sigma,
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&kitti_calibration.average_delta_t);
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printf("IMU metadata: %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\n",
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kitti_calibration.body_ptx, kitti_calibration.body_pty,
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kitti_calibration.body_ptz, kitti_calibration.body_prx,
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kitti_calibration.body_pry, kitti_calibration.body_prz,
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kitti_calibration.accelerometer_sigma,
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kitti_calibration.gyroscope_sigma, kitti_calibration.integration_sigma,
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kitti_calibration.accelerometer_bias_sigma,
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kitti_calibration.gyroscope_bias_sigma,
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kitti_calibration.average_delta_t);
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// Read IMU data
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// Time dt accelX accelY accelZ omegaX omegaY omegaZ
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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,
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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,
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&acc_x, &acc_y, &acc_z, &gyro_x, &gyro_y, &gyro_z);
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ImuMeasurement 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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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 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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}
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int main(int argc, char* argv[]) {
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KittiCalibration kitti_calibration;
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vector<ImuMeasurement> imu_measurements;
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vector<GpsMeasurement> gps_measurements;
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loadKittiData(kitti_calibration, imu_measurements, gps_measurements);
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Vector6 BodyP =
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(Vector6() << kitti_calibration.body_ptx, kitti_calibration.body_pty,
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kitti_calibration.body_ptz, kitti_calibration.body_prx,
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kitti_calibration.body_pry, kitti_calibration.body_prz)
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.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(
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"Currently only support IMUinBody is identity, i.e. IMU and body frame "
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"are the same");
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exit(-1);
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}
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// Configure different variables
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// double t_offset = gps_measurements[0].time;
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size_t first_gps_pose = 1;
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size_t gps_skip = 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(); // zero vector
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// Configure noise models
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auto noise_model_gps = noiseModel::Diagonal::Precisions(
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(Vector6() << Vector3::Constant(0), Vector3::Constant(1.0 / 0.07))
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.finished());
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// Set initial conditions for the estimated trajectory
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// initial pose is the reference frame (navigation frame)
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auto current_pose_global =
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Pose3(Rot3(), gps_measurements[first_gps_pose].position);
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// the vehicle is stationary at the beginning at position 0,0,0
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Vector3 current_velocity_global = Vector3::Zero();
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auto current_bias = imuBias::ConstantBias(); // init with zero bias
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auto sigma_init_x = noiseModel::Diagonal::Precisions(
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(Vector6() << Vector3::Constant(0), Vector3::Constant(1.0)).finished());
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auto sigma_init_v = noiseModel::Diagonal::Sigmas(Vector3::Constant(1000.0));
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auto sigma_init_b = noiseModel::Diagonal::Sigmas(
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(Vector6() << Vector3::Constant(0.100), Vector3::Constant(5.00e-05))
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.finished());
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// Set IMU preintegration parameters
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Matrix33 measured_acc_cov =
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I_3x3 * pow(kitti_calibration.accelerometer_sigma, 2);
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Matrix33 measured_omega_cov =
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I_3x3 * pow(kitti_calibration.gyroscope_sigma, 2);
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// error committed in integrating position from velocities
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Matrix33 integration_error_cov =
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I_3x3 * pow(kitti_calibration.integration_sigma, 2);
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auto imu_params = PreintegratedImuMeasurements::Params::MakeSharedU(g);
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imu_params->accelerometerCovariance =
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measured_acc_cov; // acc white noise in continuous
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imu_params->integrationCovariance =
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integration_error_cov; // integration uncertainty continuous
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imu_params->gyroscopeCovariance =
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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> current_summarized_measurement =
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nullptr;
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// Set ISAM2 parameters and create ISAM2 solver object
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ISAM2Params isam_params;
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isam_params.factorization = ISAM2Params::CHOLESKY;
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isam_params.relinearizeSkip = 10;
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ISAM2 isam(isam_params);
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// Create the factor graph and values object that will store new factors and
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// values to add to the incremental graph
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NonlinearFactorGraph new_factors;
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Values new_values; // values storing the initial estimates of new nodes in
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// 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(
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"-- Starting main loop: inference is performed at each time step, but we "
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"plot trajectory every 10 steps\n");
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size_t j = 0;
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size_t included_imu_measurement_count = 0;
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for (size_t i = first_gps_pose; i < gps_measurements.size() - 1; i++) {
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// At each non=IMU measurement we initialize a new node in the graph
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auto current_pose_key = X(i);
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auto current_vel_key = V(i);
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auto current_bias_key = B(i);
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double t = gps_measurements[i].time;
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if (i == first_gps_pose) {
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// Create initial estimate and prior on initial pose, velocity, and biases
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new_values.insert(current_pose_key, current_pose_global);
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new_values.insert(current_vel_key, current_velocity_global);
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new_values.insert(current_bias_key, current_bias);
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new_factors.emplace_shared<PriorFactor<Pose3>>(
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current_pose_key, current_pose_global, sigma_init_x);
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new_factors.emplace_shared<PriorFactor<Vector3>>(
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current_vel_key, current_velocity_global, sigma_init_v);
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new_factors.emplace_shared<PriorFactor<imuBias::ConstantBias>>(
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current_bias_key, current_bias, sigma_init_b);
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} else {
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double t_previous = gps_measurements[i - 1].time;
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// Summarize IMU data between the previous GPS measurement and now
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current_summarized_measurement =
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std::make_shared<PreintegratedImuMeasurements>(imu_params,
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current_bias);
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while (j < imu_measurements.size() && imu_measurements[j].time <= t) {
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if (imu_measurements[j].time >= t_previous) {
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current_summarized_measurement->integrateMeasurement(
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imu_measurements[j].accelerometer, imu_measurements[j].gyroscope,
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imu_measurements[j].dt);
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included_imu_measurement_count++;
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}
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j++;
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}
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// Create IMU factor
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auto previous_pose_key = X(i - 1);
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auto previous_vel_key = V(i - 1);
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auto previous_bias_key = B(i - 1);
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new_factors.emplace_shared<ImuFactor>(
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previous_pose_key, previous_vel_key, current_pose_key,
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current_vel_key, previous_bias_key, *current_summarized_measurement);
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// Bias evolution as given in the IMU metadata
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auto sigma_between_b = noiseModel::Diagonal::Sigmas(
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(Vector6() << Vector3::Constant(
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sqrt(included_imu_measurement_count) *
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kitti_calibration.accelerometer_bias_sigma),
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Vector3::Constant(sqrt(included_imu_measurement_count) *
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kitti_calibration.gyroscope_bias_sigma))
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.finished());
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new_factors.emplace_shared<BetweenFactor<imuBias::ConstantBias>>(
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previous_bias_key, current_bias_key, imuBias::ConstantBias(),
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sigma_between_b);
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// Create GPS factor
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auto gps_pose =
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Pose3(current_pose_global.rotation(), gps_measurements[i].position);
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if ((i % gps_skip) == 0) {
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new_factors.emplace_shared<PriorFactor<Pose3>>(
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current_pose_key, gps_pose, noise_model_gps);
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new_values.insert(current_pose_key, gps_pose);
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printf("############ POSE INCLUDED AT TIME %.6lf ############\n",
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t);
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cout << gps_pose.translation();
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printf("\n\n");
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} else {
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new_values.insert(current_pose_key, current_pose_global);
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}
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// Add initial values for velocity and bias based on the previous
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// estimates
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new_values.insert(current_vel_key, current_velocity_global);
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new_values.insert(current_bias_key, current_bias);
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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 (i > (first_gps_pose + 2 * gps_skip)) {
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printf("############ NEW FACTORS AT TIME %.6lf ############\n",
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t);
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new_factors.print();
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isam.update(new_factors, new_values);
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// Reset the newFactors and newValues list
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new_factors.resize(0);
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new_values.clear();
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// Extract the result/current estimates
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Values result = isam.calculateEstimate();
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current_pose_global = result.at<Pose3>(current_pose_key);
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current_velocity_global = result.at<Vector3>(current_vel_key);
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current_bias = result.at<imuBias::ConstantBias>(current_bias_key);
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printf("\n############ POSE AT TIME %lf ############\n", t);
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current_pose_global.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,
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"#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 i = first_gps_pose; i < gps_measurements.size() - 1; i++) {
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auto pose_key = X(i);
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auto vel_key = V(i);
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auto bias_key = B(i);
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auto pose = result.at<Pose3>(pose_key);
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auto velocity = result.at<Vector3>(vel_key);
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auto bias = result.at<imuBias::ConstantBias>(bias_key);
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auto pose_quat = pose.rotation().toQuaternion();
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auto gps = gps_measurements[i].position;
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cout << "State at #" << i << endl;
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cout << "Pose:" << endl << pose << endl;
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cout << "Velocity:" << endl << velocity << endl;
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cout << "Bias:" << endl << bias << endl;
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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[i].time, pose.x(), pose.y(), pose.z(),
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pose_quat.x(), pose_quat.y(), pose_quat.z(), pose_quat.w(), gps(0),
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gps(1), gps(2));
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}
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fclose(fp_out);
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}
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