Merge pull request #836 from borglab/fix/566
Varun Agrawal
GitHub
commit
791c7bdcab
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@ -11,21 +11,23 @@
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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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* @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/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/NonlinearFactorGraph.h>
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#include <gtsam/inference/Symbol.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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@ -34,35 +36,35 @@
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using namespace std;
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using namespace gtsam;
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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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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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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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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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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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@ -70,290 +72,313 @@ 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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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
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// AccelerometerBiasSigma GyroscopeBiasSigma AverageDeltaT
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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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// 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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printf("-- Reading sensor metadata\n");
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getline(imu_metadata, line, '\n'); // ignore the first line
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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,
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&kitti_calibration.body_pty,
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&kitti_calibration.body_ptz,
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&kitti_calibration.body_prx,
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&kitti_calibration.body_pry,
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&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,
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kitti_calibration.body_pty,
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kitti_calibration.body_ptz,
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kitti_calibration.body_prx,
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kitti_calibration.body_pry,
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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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// 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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// 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, 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",
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&time, &dt,
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&acc_x, &acc_y, &acc_z,
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&gyro_x, &gyro_y, &gyro_z);
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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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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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// 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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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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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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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 = (Vector6() << kitti_calibration.body_ptx, kitti_calibration.body_pty, kitti_calibration.body_ptz,
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kitti_calibration.body_prx, 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("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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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 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((Vector6() << Vector3::Constant(0),
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Vector3::Constant(1.0/0.07))
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.finished());
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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 = 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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// 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((Vector6() << Vector3::Constant(0),
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Vector3::Constant(1.0))
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.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((Vector6() << Vector3::Constant(0.100),
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Vector3::Constant(5.00e-05))
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.finished());
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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 = I_3x3 * pow(kitti_calibration.accelerometer_sigma, 2);
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Matrix33 measured_omega_cov = 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 = I_3x3 * pow(kitti_calibration.integration_sigma, 2);
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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 = 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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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 = nullptr;
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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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// 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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ISAM2 isam(isam_params);
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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 new_factors;
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Values new_values; // values storing the initial estimates of new nodes in the factor graph
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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
|
||||
printf("-- Starting main loop: inference is performed at each time step, but we plot trajectory every 10 steps\n");
|
||||
size_t j = 0;
|
||||
for (size_t i = first_gps_pose; i < gps_measurements.size() - 1; i++) {
|
||||
// At each non=IMU measurement we initialize a new node in the graph
|
||||
auto current_pose_key = X(i);
|
||||
auto current_vel_key = V(i);
|
||||
auto current_bias_key = B(i);
|
||||
double t = gps_measurements[i].time;
|
||||
/// Main loop:
|
||||
/// (1) we read the measurements
|
||||
/// (2) we create the corresponding factors in the graph
|
||||
/// (3) we solve the graph to obtain and optimal estimate of robot trajectory
|
||||
printf(
|
||||
"-- Starting main loop: inference is performed at each time step, but we "
|
||||
"plot trajectory every 10 steps\n");
|
||||
size_t j = 0;
|
||||
size_t included_imu_measurement_count = 0;
|
||||
|
||||
if (i == first_gps_pose) {
|
||||
// Create initial estimate and prior on initial pose, velocity, and biases
|
||||
new_values.insert(current_pose_key, current_pose_global);
|
||||
new_values.insert(current_vel_key, current_velocity_global);
|
||||
new_values.insert(current_bias_key, current_bias);
|
||||
new_factors.emplace_shared<PriorFactor<Pose3>>(current_pose_key, current_pose_global, sigma_init_x);
|
||||
new_factors.emplace_shared<PriorFactor<Vector3>>(current_vel_key, current_velocity_global, sigma_init_v);
|
||||
new_factors.emplace_shared<PriorFactor<imuBias::ConstantBias>>(current_bias_key, current_bias, sigma_init_b);
|
||||
} else {
|
||||
double t_previous = gps_measurements[i-1].time;
|
||||
for (size_t i = first_gps_pose; i < gps_measurements.size() - 1; i++) {
|
||||
// At each non=IMU measurement we initialize a new node in the graph
|
||||
auto current_pose_key = X(i);
|
||||
auto current_vel_key = V(i);
|
||||
auto current_bias_key = B(i);
|
||||
double t = gps_measurements[i].time;
|
||||
|
||||
// Summarize IMU data between the previous GPS measurement and now
|
||||
current_summarized_measurement = std::make_shared<PreintegratedImuMeasurements>(imu_params, current_bias);
|
||||
static size_t included_imu_measurement_count = 0;
|
||||
while (j < imu_measurements.size() && imu_measurements[j].time <= t) {
|
||||
if (imu_measurements[j].time >= t_previous) {
|
||||
current_summarized_measurement->integrateMeasurement(imu_measurements[j].accelerometer,
|
||||
imu_measurements[j].gyroscope,
|
||||
imu_measurements[j].dt);
|
||||
included_imu_measurement_count++;
|
||||
}
|
||||
j++;
|
||||
}
|
||||
if (i == first_gps_pose) {
|
||||
// Create initial estimate and prior on initial pose, velocity, and biases
|
||||
new_values.insert(current_pose_key, current_pose_global);
|
||||
new_values.insert(current_vel_key, current_velocity_global);
|
||||
new_values.insert(current_bias_key, current_bias);
|
||||
new_factors.emplace_shared<PriorFactor<Pose3>>(
|
||||
current_pose_key, current_pose_global, sigma_init_x);
|
||||
new_factors.emplace_shared<PriorFactor<Vector3>>(
|
||||
current_vel_key, current_velocity_global, sigma_init_v);
|
||||
new_factors.emplace_shared<PriorFactor<imuBias::ConstantBias>>(
|
||||
current_bias_key, current_bias, sigma_init_b);
|
||||
} else {
|
||||
double t_previous = gps_measurements[i - 1].time;
|
||||
|
||||
// Create IMU factor
|
||||
auto previous_pose_key = X(i-1);
|
||||
auto previous_vel_key = V(i-1);
|
||||
auto previous_bias_key = B(i-1);
|
||||
// Summarize IMU data between the previous GPS measurement and now
|
||||
current_summarized_measurement =
|
||||
std::make_shared<PreintegratedImuMeasurements>(imu_params,
|
||||
current_bias);
|
||||
|
||||
new_factors.emplace_shared<ImuFactor>(previous_pose_key, previous_vel_key,
|
||||
current_pose_key, current_vel_key,
|
||||
previous_bias_key, *current_summarized_measurement);
|
||||
|
||||
// Bias evolution as given in the IMU metadata
|
||||
auto sigma_between_b = noiseModel::Diagonal::Sigmas((Vector6() <<
|
||||
Vector3::Constant(sqrt(included_imu_measurement_count) * kitti_calibration.accelerometer_bias_sigma),
|
||||
Vector3::Constant(sqrt(included_imu_measurement_count) * kitti_calibration.gyroscope_bias_sigma))
|
||||
.finished());
|
||||
new_factors.emplace_shared<BetweenFactor<imuBias::ConstantBias>>(previous_bias_key,
|
||||
current_bias_key,
|
||||
imuBias::ConstantBias(),
|
||||
sigma_between_b);
|
||||
|
||||
// Create GPS factor
|
||||
auto gps_pose = Pose3(current_pose_global.rotation(), gps_measurements[i].position);
|
||||
if ((i % gps_skip) == 0) {
|
||||
new_factors.emplace_shared<PriorFactor<Pose3>>(current_pose_key, gps_pose, noise_model_gps);
|
||||
new_values.insert(current_pose_key, gps_pose);
|
||||
|
||||
printf("################ POSE INCLUDED AT TIME %lf ################\n", t);
|
||||
cout << gps_pose.translation();
|
||||
printf("\n\n");
|
||||
} else {
|
||||
new_values.insert(current_pose_key, current_pose_global);
|
||||
}
|
||||
|
||||
// Add initial values for velocity and bias based on the previous estimates
|
||||
new_values.insert(current_vel_key, current_velocity_global);
|
||||
new_values.insert(current_bias_key, current_bias);
|
||||
|
||||
// Update solver
|
||||
// =======================================================================
|
||||
// We accumulate 2*GPSskip GPS measurements before updating the solver at
|
||||
// first so that the heading becomes observable.
|
||||
if (i > (first_gps_pose + 2*gps_skip)) {
|
||||
printf("################ NEW FACTORS AT TIME %lf ################\n", t);
|
||||
new_factors.print();
|
||||
|
||||
isam.update(new_factors, new_values);
|
||||
|
||||
// Reset the newFactors and newValues list
|
||||
new_factors.resize(0);
|
||||
new_values.clear();
|
||||
|
||||
// Extract the result/current estimates
|
||||
Values result = isam.calculateEstimate();
|
||||
|
||||
current_pose_global = result.at<Pose3>(current_pose_key);
|
||||
current_velocity_global = result.at<Vector3>(current_vel_key);
|
||||
current_bias = result.at<imuBias::ConstantBias>(current_bias_key);
|
||||
|
||||
printf("\n################ POSE AT TIME %lf ################\n", t);
|
||||
current_pose_global.print();
|
||||
printf("\n\n");
|
||||
}
|
||||
while (j < imu_measurements.size() && imu_measurements[j].time <= t) {
|
||||
if (imu_measurements[j].time >= t_previous) {
|
||||
current_summarized_measurement->integrateMeasurement(
|
||||
imu_measurements[j].accelerometer, imu_measurements[j].gyroscope,
|
||||
imu_measurements[j].dt);
|
||||
included_imu_measurement_count++;
|
||||
}
|
||||
j++;
|
||||
}
|
||||
|
||||
// Create IMU factor
|
||||
auto previous_pose_key = X(i - 1);
|
||||
auto previous_vel_key = V(i - 1);
|
||||
auto previous_bias_key = B(i - 1);
|
||||
|
||||
new_factors.emplace_shared<ImuFactor>(
|
||||
previous_pose_key, previous_vel_key, current_pose_key,
|
||||
current_vel_key, previous_bias_key, *current_summarized_measurement);
|
||||
|
||||
// Bias evolution as given in the IMU metadata
|
||||
auto sigma_between_b = noiseModel::Diagonal::Sigmas(
|
||||
(Vector6() << Vector3::Constant(
|
||||
sqrt(included_imu_measurement_count) *
|
||||
kitti_calibration.accelerometer_bias_sigma),
|
||||
Vector3::Constant(sqrt(included_imu_measurement_count) *
|
||||
kitti_calibration.gyroscope_bias_sigma))
|
||||
.finished());
|
||||
new_factors.emplace_shared<BetweenFactor<imuBias::ConstantBias>>(
|
||||
previous_bias_key, current_bias_key, imuBias::ConstantBias(),
|
||||
sigma_between_b);
|
||||
|
||||
// Create GPS factor
|
||||
auto gps_pose =
|
||||
Pose3(current_pose_global.rotation(), gps_measurements[i].position);
|
||||
if ((i % gps_skip) == 0) {
|
||||
new_factors.emplace_shared<PriorFactor<Pose3>>(
|
||||
current_pose_key, gps_pose, noise_model_gps);
|
||||
new_values.insert(current_pose_key, gps_pose);
|
||||
|
||||
printf("############ POSE INCLUDED AT TIME %.6lf ############\n",
|
||||
t);
|
||||
cout << gps_pose.translation();
|
||||
printf("\n\n");
|
||||
} else {
|
||||
new_values.insert(current_pose_key, current_pose_global);
|
||||
}
|
||||
|
||||
// Add initial values for velocity and bias based on the previous
|
||||
// estimates
|
||||
new_values.insert(current_vel_key, current_velocity_global);
|
||||
new_values.insert(current_bias_key, current_bias);
|
||||
|
||||
// Update solver
|
||||
// =======================================================================
|
||||
// We accumulate 2*GPSskip GPS measurements before updating the solver at
|
||||
// first so that the heading becomes observable.
|
||||
if (i > (first_gps_pose + 2 * gps_skip)) {
|
||||
printf("############ NEW FACTORS AT TIME %.6lf ############\n",
|
||||
t);
|
||||
new_factors.print();
|
||||
|
||||
isam.update(new_factors, new_values);
|
||||
|
||||
// Reset the newFactors and newValues list
|
||||
new_factors.resize(0);
|
||||
new_values.clear();
|
||||
|
||||
// Extract the result/current estimates
|
||||
Values result = isam.calculateEstimate();
|
||||
|
||||
current_pose_global = result.at<Pose3>(current_pose_key);
|
||||
current_velocity_global = result.at<Vector3>(current_vel_key);
|
||||
current_bias = result.at<imuBias::ConstantBias>(current_bias_key);
|
||||
|
||||
printf("\n############ POSE AT TIME %lf ############\n", t);
|
||||
current_pose_global.print();
|
||||
printf("\n\n");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Save results to file
|
||||
printf("\nWriting results to file...\n");
|
||||
FILE* fp_out = fopen(output_filename.c_str(), "w+");
|
||||
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");
|
||||
// Save results to file
|
||||
printf("\nWriting results to file...\n");
|
||||
FILE* fp_out = fopen(output_filename.c_str(), "w+");
|
||||
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");
|
||||
|
||||
Values result = isam.calculateEstimate();
|
||||
for (size_t i = first_gps_pose; i < gps_measurements.size() - 1; i++) {
|
||||
auto pose_key = X(i);
|
||||
auto vel_key = V(i);
|
||||
auto bias_key = B(i);
|
||||
Values result = isam.calculateEstimate();
|
||||
for (size_t i = first_gps_pose; i < gps_measurements.size() - 1; i++) {
|
||||
auto pose_key = X(i);
|
||||
auto vel_key = V(i);
|
||||
auto bias_key = B(i);
|
||||
|
||||
auto pose = result.at<Pose3>(pose_key);
|
||||
auto velocity = result.at<Vector3>(vel_key);
|
||||
auto bias = result.at<imuBias::ConstantBias>(bias_key);
|
||||
auto pose = result.at<Pose3>(pose_key);
|
||||
auto velocity = result.at<Vector3>(vel_key);
|
||||
auto bias = result.at<imuBias::ConstantBias>(bias_key);
|
||||
|
||||
auto pose_quat = pose.rotation().toQuaternion();
|
||||
auto gps = gps_measurements[i].position;
|
||||
auto pose_quat = pose.rotation().toQuaternion();
|
||||
auto gps = gps_measurements[i].position;
|
||||
|
||||
cout << "State at #" << i << endl;
|
||||
cout << "Pose:" << endl << pose << endl;
|
||||
cout << "Velocity:" << endl << velocity << endl;
|
||||
cout << "Bias:" << endl << bias << endl;
|
||||
cout << "State at #" << i << endl;
|
||||
cout << "Pose:" << endl << pose << endl;
|
||||
cout << "Velocity:" << endl << velocity << endl;
|
||||
cout << "Bias:" << endl << bias << endl;
|
||||
|
||||
fprintf(fp_out, "%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n",
|
||||
gps_measurements[i].time,
|
||||
pose.x(), pose.y(), pose.z(),
|
||||
pose_quat.x(), pose_quat.y(), pose_quat.z(), pose_quat.w(),
|
||||
gps(0), gps(1), gps(2));
|
||||
}
|
||||
fprintf(fp_out, "%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n",
|
||||
gps_measurements[i].time, pose.x(), pose.y(), pose.z(),
|
||||
pose_quat.x(), pose_quat.y(), pose_quat.z(), pose_quat.w(), gps(0),
|
||||
gps(1), gps(2));
|
||||
}
|
||||
|
||||
fclose(fp_out);
|
||||
fclose(fp_out);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,366 @@
|
|||
"""
|
||||
Example of application of ISAM2 for GPS-aided navigation on the KITTI VISION BENCHMARK SUITE
|
||||
|
||||
Author: Varun Agrawal
|
||||
"""
|
||||
import argparse
|
||||
from typing import List, Tuple
|
||||
|
||||
import gtsam
|
||||
import numpy as np
|
||||
from gtsam import ISAM2, Pose3, noiseModel
|
||||
from gtsam.symbol_shorthand import B, V, X
|
||||
|
||||
GRAVITY = 9.8
|
||||
|
||||
|
||||
class KittiCalibration:
|
||||
"""Class to hold KITTI calibration info."""
|
||||
def __init__(self, body_ptx: float, body_pty: float, body_ptz: float,
|
||||
body_prx: float, body_pry: float, body_prz: float,
|
||||
accelerometer_sigma: float, gyroscope_sigma: float,
|
||||
integration_sigma: float, accelerometer_bias_sigma: float,
|
||||
gyroscope_bias_sigma: float, average_delta_t: float):
|
||||
self.bodyTimu = Pose3(gtsam.Rot3.RzRyRx(body_prx, body_pry, body_prz),
|
||||
gtsam.Point3(body_ptx, body_pty, body_ptz))
|
||||
self.accelerometer_sigma = accelerometer_sigma
|
||||
self.gyroscope_sigma = gyroscope_sigma
|
||||
self.integration_sigma = integration_sigma
|
||||
self.accelerometer_bias_sigma = accelerometer_bias_sigma
|
||||
self.gyroscope_bias_sigma = gyroscope_bias_sigma
|
||||
self.average_delta_t = average_delta_t
|
||||
|
||||
|
||||
class ImuMeasurement:
|
||||
"""An instance of an IMU measurement."""
|
||||
def __init__(self, time: float, dt: float, accelerometer: gtsam.Point3,
|
||||
gyroscope: gtsam.Point3):
|
||||
self.time = time
|
||||
self.dt = dt
|
||||
self.accelerometer = accelerometer
|
||||
self.gyroscope = gyroscope
|
||||
|
||||
|
||||
class GpsMeasurement:
|
||||
"""An instance of a GPS measurement."""
|
||||
def __init__(self, time: float, position: gtsam.Point3):
|
||||
self.time = time
|
||||
self.position = position
|
||||
|
||||
|
||||
def loadImuData(imu_data_file: str) -> List[ImuMeasurement]:
|
||||
"""Helper to load the IMU data."""
|
||||
# Read IMU data
|
||||
# Time dt accelX accelY accelZ omegaX omegaY omegaZ
|
||||
imu_data_file = gtsam.findExampleDataFile(imu_data_file)
|
||||
imu_measurements = []
|
||||
|
||||
print("-- Reading IMU measurements from file")
|
||||
with open(imu_data_file, encoding='UTF-8') as imu_data:
|
||||
data = imu_data.readlines()
|
||||
for i in range(1, len(data)): # ignore the first line
|
||||
time, dt, acc_x, acc_y, acc_z, gyro_x, gyro_y, gyro_z = map(
|
||||
float, data[i].split(' '))
|
||||
imu_measurement = ImuMeasurement(
|
||||
time, dt, np.asarray([acc_x, acc_y, acc_z]),
|
||||
np.asarray([gyro_x, gyro_y, gyro_z]))
|
||||
imu_measurements.append(imu_measurement)
|
||||
|
||||
return imu_measurements
|
||||
|
||||
|
||||
def loadGpsData(gps_data_file: str) -> List[GpsMeasurement]:
|
||||
"""Helper to load the GPS data."""
|
||||
# Read GPS data
|
||||
# Time,X,Y,Z
|
||||
gps_data_file = gtsam.findExampleDataFile(gps_data_file)
|
||||
gps_measurements = []
|
||||
|
||||
print("-- Reading GPS measurements from file")
|
||||
with open(gps_data_file, encoding='UTF-8') as gps_data:
|
||||
data = gps_data.readlines()
|
||||
for i in range(1, len(data)):
|
||||
time, x, y, z = map(float, data[i].split(','))
|
||||
gps_measurement = GpsMeasurement(time, np.asarray([x, y, z]))
|
||||
gps_measurements.append(gps_measurement)
|
||||
|
||||
return gps_measurements
|
||||
|
||||
|
||||
def loadKittiData(
|
||||
imu_data_file: str = "KittiEquivBiasedImu.txt",
|
||||
gps_data_file: str = "KittiGps_converted.txt",
|
||||
imu_metadata_file: str = "KittiEquivBiasedImu_metadata.txt"
|
||||
) -> Tuple[KittiCalibration, List[ImuMeasurement], List[GpsMeasurement]]:
|
||||
"""
|
||||
Load the KITTI Dataset.
|
||||
"""
|
||||
# Read IMU metadata and compute relative sensor pose transforms
|
||||
# BodyPtx BodyPty BodyPtz BodyPrx BodyPry BodyPrz AccelerometerSigma
|
||||
# GyroscopeSigma IntegrationSigma AccelerometerBiasSigma GyroscopeBiasSigma
|
||||
# AverageDeltaT
|
||||
imu_metadata_file = gtsam.findExampleDataFile(imu_metadata_file)
|
||||
with open(imu_metadata_file, encoding='UTF-8') as imu_metadata:
|
||||
print("-- Reading sensor metadata")
|
||||
line = imu_metadata.readline() # Ignore the first line
|
||||
line = imu_metadata.readline().strip()
|
||||
data = list(map(float, line.split(' ')))
|
||||
kitti_calibration = KittiCalibration(*data)
|
||||
print("IMU metadata:", data)
|
||||
|
||||
imu_measurements = loadImuData(imu_data_file)
|
||||
gps_measurements = loadGpsData(gps_data_file)
|
||||
|
||||
return kitti_calibration, imu_measurements, gps_measurements
|
||||
|
||||
|
||||
def getImuParams(kitti_calibration: KittiCalibration):
|
||||
"""Get the IMU parameters from the KITTI calibration data."""
|
||||
w_coriolis = np.zeros(3)
|
||||
|
||||
# Set IMU preintegration parameters
|
||||
measured_acc_cov = np.eye(3) * np.power(
|
||||
kitti_calibration.accelerometer_sigma, 2)
|
||||
measured_omega_cov = np.eye(3) * np.power(
|
||||
kitti_calibration.gyroscope_sigma, 2)
|
||||
# error committed in integrating position from velocities
|
||||
integration_error_cov = np.eye(3) * np.power(
|
||||
kitti_calibration.integration_sigma, 2)
|
||||
|
||||
imu_params = gtsam.PreintegrationParams.MakeSharedU(GRAVITY)
|
||||
# acc white noise in continuous
|
||||
imu_params.setAccelerometerCovariance(measured_acc_cov)
|
||||
# integration uncertainty continuous
|
||||
imu_params.setIntegrationCovariance(integration_error_cov)
|
||||
# gyro white noise in continuous
|
||||
imu_params.setGyroscopeCovariance(measured_omega_cov)
|
||||
imu_params.setOmegaCoriolis(w_coriolis)
|
||||
|
||||
return imu_params
|
||||
|
||||
|
||||
def save_results(isam: gtsam.ISAM2, output_filename: str, first_gps_pose: int,
|
||||
gps_measurements: List[GpsMeasurement]):
|
||||
"""Write the results from `isam` to `output_filename`."""
|
||||
# Save results to file
|
||||
print("Writing results to file...")
|
||||
with open(output_filename, 'w', encoding='UTF-8') as fp_out:
|
||||
fp_out.write(
|
||||
"#time(s),x(m),y(m),z(m),qx,qy,qz,qw,gt_x(m),gt_y(m),gt_z(m)\n")
|
||||
|
||||
result = isam.calculateEstimate()
|
||||
for i in range(first_gps_pose, len(gps_measurements)):
|
||||
pose_key = X(i)
|
||||
vel_key = V(i)
|
||||
bias_key = B(i)
|
||||
|
||||
pose = result.atPose3(pose_key)
|
||||
velocity = result.atVector(vel_key)
|
||||
bias = result.atConstantBias(bias_key)
|
||||
|
||||
pose_quat = pose.rotation().toQuaternion()
|
||||
gps = gps_measurements[i].position
|
||||
|
||||
print(f"State at #{i}")
|
||||
print(f"Pose:\n{pose}")
|
||||
print(f"Velocity:\n{velocity}")
|
||||
print(f"Bias:\n{bias}")
|
||||
|
||||
fp_out.write("{},{},{},{},{},{},{},{},{},{},{}\n".format(
|
||||
gps_measurements[i].time, pose.x(), pose.y(), pose.z(),
|
||||
pose_quat.x(), pose_quat.y(), pose_quat.z(), pose_quat.w(),
|
||||
gps[0], gps[1], gps[2]))
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""Parse command line arguments."""
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--output_filename",
|
||||
default="IMUKittiExampleGPSResults.csv")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def optimize(gps_measurements: List[GpsMeasurement],
|
||||
imu_measurements: List[ImuMeasurement],
|
||||
sigma_init_x: gtsam.noiseModel.Diagonal,
|
||||
sigma_init_v: gtsam.noiseModel.Diagonal,
|
||||
sigma_init_b: gtsam.noiseModel.Diagonal,
|
||||
noise_model_gps: gtsam.noiseModel.Diagonal,
|
||||
kitti_calibration: KittiCalibration, first_gps_pose: int,
|
||||
gps_skip: int) -> gtsam.ISAM2:
|
||||
"""Run ISAM2 optimization on the measurements."""
|
||||
# Set initial conditions for the estimated trajectory
|
||||
# initial pose is the reference frame (navigation frame)
|
||||
current_pose_global = Pose3(gtsam.Rot3(),
|
||||
gps_measurements[first_gps_pose].position)
|
||||
|
||||
# the vehicle is stationary at the beginning at position 0,0,0
|
||||
current_velocity_global = np.zeros(3)
|
||||
current_bias = gtsam.imuBias.ConstantBias() # init with zero bias
|
||||
|
||||
imu_params = getImuParams(kitti_calibration)
|
||||
|
||||
# Set ISAM2 parameters and create ISAM2 solver object
|
||||
isam_params = gtsam.ISAM2Params()
|
||||
isam_params.setFactorization("CHOLESKY")
|
||||
isam_params.setRelinearizeSkip(10)
|
||||
|
||||
isam = gtsam.ISAM2(isam_params)
|
||||
|
||||
# Create the factor graph and values object that will store new factors and
|
||||
# values to add to the incremental graph
|
||||
new_factors = gtsam.NonlinearFactorGraph()
|
||||
# values storing the initial estimates of new nodes in the factor graph
|
||||
new_values = gtsam.Values()
|
||||
|
||||
# Main loop:
|
||||
# (1) we read the measurements
|
||||
# (2) we create the corresponding factors in the graph
|
||||
# (3) we solve the graph to obtain and optimal estimate of robot trajectory
|
||||
print("-- Starting main loop: inference is performed at each time step, "
|
||||
"but we plot trajectory every 10 steps")
|
||||
|
||||
j = 0
|
||||
included_imu_measurement_count = 0
|
||||
|
||||
for i in range(first_gps_pose, len(gps_measurements)):
|
||||
# At each non=IMU measurement we initialize a new node in the graph
|
||||
current_pose_key = X(i)
|
||||
current_vel_key = V(i)
|
||||
current_bias_key = B(i)
|
||||
t = gps_measurements[i].time
|
||||
|
||||
if i == first_gps_pose:
|
||||
# Create initial estimate and prior on initial pose, velocity, and biases
|
||||
new_values.insert(current_pose_key, current_pose_global)
|
||||
new_values.insert(current_vel_key, current_velocity_global)
|
||||
new_values.insert(current_bias_key, current_bias)
|
||||
|
||||
new_factors.addPriorPose3(current_pose_key, current_pose_global,
|
||||
sigma_init_x)
|
||||
new_factors.addPriorVector(current_vel_key,
|
||||
current_velocity_global, sigma_init_v)
|
||||
new_factors.addPriorConstantBias(current_bias_key, current_bias,
|
||||
sigma_init_b)
|
||||
else:
|
||||
t_previous = gps_measurements[i - 1].time
|
||||
|
||||
# Summarize IMU data between the previous GPS measurement and now
|
||||
current_summarized_measurement = gtsam.PreintegratedImuMeasurements(
|
||||
imu_params, current_bias)
|
||||
|
||||
while (j < len(imu_measurements)
|
||||
and imu_measurements[j].time <= t):
|
||||
if imu_measurements[j].time >= t_previous:
|
||||
current_summarized_measurement.integrateMeasurement(
|
||||
imu_measurements[j].accelerometer,
|
||||
imu_measurements[j].gyroscope, imu_measurements[j].dt)
|
||||
included_imu_measurement_count += 1
|
||||
j += 1
|
||||
|
||||
# Create IMU factor
|
||||
previous_pose_key = X(i - 1)
|
||||
previous_vel_key = V(i - 1)
|
||||
previous_bias_key = B(i - 1)
|
||||
|
||||
new_factors.push_back(
|
||||
gtsam.ImuFactor(previous_pose_key, previous_vel_key,
|
||||
current_pose_key, current_vel_key,
|
||||
previous_bias_key,
|
||||
current_summarized_measurement))
|
||||
|
||||
# Bias evolution as given in the IMU metadata
|
||||
sigma_between_b = gtsam.noiseModel.Diagonal.Sigmas(
|
||||
np.asarray([
|
||||
np.sqrt(included_imu_measurement_count) *
|
||||
kitti_calibration.accelerometer_bias_sigma
|
||||
] * 3 + [
|
||||
np.sqrt(included_imu_measurement_count) *
|
||||
kitti_calibration.gyroscope_bias_sigma
|
||||
] * 3))
|
||||
|
||||
new_factors.push_back(
|
||||
gtsam.BetweenFactorConstantBias(previous_bias_key,
|
||||
current_bias_key,
|
||||
gtsam.imuBias.ConstantBias(),
|
||||
sigma_between_b))
|
||||
|
||||
# Create GPS factor
|
||||
gps_pose = Pose3(current_pose_global.rotation(),
|
||||
gps_measurements[i].position)
|
||||
if (i % gps_skip) == 0:
|
||||
new_factors.addPriorPose3(current_pose_key, gps_pose,
|
||||
noise_model_gps)
|
||||
new_values.insert(current_pose_key, gps_pose)
|
||||
|
||||
print(f"############ POSE INCLUDED AT TIME {t} ############")
|
||||
print(gps_pose.translation(), "\n")
|
||||
else:
|
||||
new_values.insert(current_pose_key, current_pose_global)
|
||||
|
||||
# Add initial values for velocity and bias based on the previous
|
||||
# estimates
|
||||
new_values.insert(current_vel_key, current_velocity_global)
|
||||
new_values.insert(current_bias_key, current_bias)
|
||||
|
||||
# Update solver
|
||||
# =======================================================================
|
||||
# We accumulate 2*GPSskip GPS measurements before updating the solver at
|
||||
# first so that the heading becomes observable.
|
||||
if i > (first_gps_pose + 2 * gps_skip):
|
||||
print(f"############ NEW FACTORS AT TIME {t:.6f} ############")
|
||||
new_factors.print()
|
||||
|
||||
isam.update(new_factors, new_values)
|
||||
|
||||
# Reset the newFactors and newValues list
|
||||
new_factors.resize(0)
|
||||
new_values.clear()
|
||||
|
||||
# Extract the result/current estimates
|
||||
result = isam.calculateEstimate()
|
||||
|
||||
current_pose_global = result.atPose3(current_pose_key)
|
||||
current_velocity_global = result.atVector(current_vel_key)
|
||||
current_bias = result.atConstantBias(current_bias_key)
|
||||
|
||||
print(f"############ POSE AT TIME {t} ############")
|
||||
current_pose_global.print()
|
||||
print("\n")
|
||||
|
||||
return isam
|
||||
|
||||
|
||||
def main():
|
||||
"""Main runner."""
|
||||
args = parse_args()
|
||||
kitti_calibration, imu_measurements, gps_measurements = loadKittiData()
|
||||
|
||||
if not kitti_calibration.bodyTimu.equals(Pose3(), 1e-8):
|
||||
raise ValueError(
|
||||
"Currently only support IMUinBody is identity, i.e. IMU and body frame are the same"
|
||||
)
|
||||
|
||||
# Configure different variables
|
||||
first_gps_pose = 1
|
||||
gps_skip = 10
|
||||
|
||||
# Configure noise models
|
||||
noise_model_gps = noiseModel.Diagonal.Precisions(
|
||||
np.asarray([0, 0, 0] + [1.0 / 0.07] * 3))
|
||||
|
||||
sigma_init_x = noiseModel.Diagonal.Precisions(
|
||||
np.asarray([0, 0, 0, 1, 1, 1]))
|
||||
sigma_init_v = noiseModel.Diagonal.Sigmas(np.ones(3) * 1000.0)
|
||||
sigma_init_b = noiseModel.Diagonal.Sigmas(
|
||||
np.asarray([0.1] * 3 + [5.00e-05] * 3))
|
||||
|
||||
isam = optimize(gps_measurements, imu_measurements, sigma_init_x,
|
||||
sigma_init_v, sigma_init_b, noise_model_gps,
|
||||
kitti_calibration, first_gps_pose, gps_skip)
|
||||
|
||||
save_results(isam, args.output_filename, first_gps_pose, gps_measurements)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in New Issue