diff --git a/examples/IMUKittiExampleGPS.cpp b/examples/IMUKittiExampleGPS.cpp new file mode 100644 index 000000000..7cfccbc11 --- /dev/null +++ b/examples/IMUKittiExampleGPS.cpp @@ -0,0 +1,353 @@ +/* ---------------------------------------------------------------------------- + + * GTSAM Copyright 2010, Georgia Tech Research Corporation, + * Atlanta, Georgia 30332-0415 + * All Rights Reserved + * Authors: Frank Dellaert, et al. (see THANKS for the full author list) + + * See LICENSE for the license information + + * -------------------------------------------------------------------------- */ + +/** + * @file IMUKittiExampleGPS + * @brief Example of application of ISAM2 for GPS-aided navigation on the KITTI VISION BENCHMARK SUITE + * @author Ported by Thomas Jespersen (thomasj@tkjelectronics.dk), TKJ Electronics + */ + +// GTSAM related includes. +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include +#include + +using namespace std; +using namespace gtsam; + +using symbol_shorthand::X; // Pose3 (x,y,z,r,p,y) +using symbol_shorthand::V; // Vel (xdot,ydot,zdot) +using symbol_shorthand::B; // Bias (ax,ay,az,gx,gy,gz) + +struct KittiCalibration { + double body_ptx; + double body_pty; + double body_ptz; + double body_prx; + double body_pry; + double body_prz; + double accelerometer_sigma; + double gyroscope_sigma; + double integration_sigma; + double accelerometer_bias_sigma; + double gyroscope_bias_sigma; + double average_delta_t; +}; + +struct ImuMeasurement { + double time; + double dt; + Vector3 accelerometer; + Vector3 gyroscope; // omega +}; + +struct GpsMeasurement { + double time; + Vector3 position; // x,y,z +}; + +const string output_filename = "IMUKittiExampleGPSResults.csv"; + +void loadKittiData(KittiCalibration& kitti_calibration, + vector& imu_measurements, + vector& gps_measurements) { + string line; + + // Read IMU metadata and compute relative sensor pose transforms + // BodyPtx BodyPty BodyPtz BodyPrx BodyPry BodyPrz AccelerometerSigma GyroscopeSigma IntegrationSigma + // AccelerometerBiasSigma GyroscopeBiasSigma AverageDeltaT + string imu_metadata_file = findExampleDataFile("KittiEquivBiasedImu_metadata.txt"); + ifstream imu_metadata(imu_metadata_file.c_str()); + + printf("-- Reading sensor metadata\n"); + + getline(imu_metadata, line, '\n'); // ignore the first line + + // Load Kitti calibration + getline(imu_metadata, line, '\n'); + sscanf(line.c_str(), "%lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf", + &kitti_calibration.body_ptx, + &kitti_calibration.body_pty, + &kitti_calibration.body_ptz, + &kitti_calibration.body_prx, + &kitti_calibration.body_pry, + &kitti_calibration.body_prz, + &kitti_calibration.accelerometer_sigma, + &kitti_calibration.gyroscope_sigma, + &kitti_calibration.integration_sigma, + &kitti_calibration.accelerometer_bias_sigma, + &kitti_calibration.gyroscope_bias_sigma, + &kitti_calibration.average_delta_t); + printf("IMU metadata: %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\n", + kitti_calibration.body_ptx, + kitti_calibration.body_pty, + kitti_calibration.body_ptz, + kitti_calibration.body_prx, + kitti_calibration.body_pry, + kitti_calibration.body_prz, + kitti_calibration.accelerometer_sigma, + kitti_calibration.gyroscope_sigma, + kitti_calibration.integration_sigma, + kitti_calibration.accelerometer_bias_sigma, + kitti_calibration.gyroscope_bias_sigma, + kitti_calibration.average_delta_t); + + // Read IMU data + // Time dt accelX accelY accelZ omegaX omegaY omegaZ + string imu_data_file = findExampleDataFile("KittiEquivBiasedImu.txt"); + printf("-- Reading IMU measurements from file\n"); + { + ifstream imu_data(imu_data_file.c_str()); + getline(imu_data, line, '\n'); // ignore the first line + + double time = 0, dt = 0, acc_x = 0, acc_y = 0, acc_z = 0, gyro_x = 0, gyro_y = 0, gyro_z = 0; + while (!imu_data.eof()) { + getline(imu_data, line, '\n'); + sscanf(line.c_str(), "%lf %lf %lf %lf %lf %lf %lf %lf", + &time, &dt, + &acc_x, &acc_y, &acc_z, + &gyro_x, &gyro_y, &gyro_z); + + ImuMeasurement measurement; + measurement.time = time; + measurement.dt = dt; + measurement.accelerometer = Vector3(acc_x, acc_y, acc_z); + measurement.gyroscope = Vector3(gyro_x, gyro_y, gyro_z); + imu_measurements.push_back(measurement); + } + } + + // Read GPS data + // Time,X,Y,Z + string gps_data_file = findExampleDataFile("KittiGps_converted.txt"); + printf("-- Reading GPS measurements from file\n"); + { + ifstream gps_data(gps_data_file.c_str()); + getline(gps_data, line, '\n'); // ignore the first line + + double time = 0, gps_x = 0, gps_y = 0, gps_z = 0; + while (!gps_data.eof()) { + getline(gps_data, line, '\n'); + sscanf(line.c_str(), "%lf,%lf,%lf,%lf", &time, &gps_x, &gps_y, &gps_z); + + GpsMeasurement measurement; + measurement.time = time; + measurement.position = Vector3(gps_x, gps_y, gps_z); + gps_measurements.push_back(measurement); + } + } +} + +int main(int argc, char* argv[]) { + KittiCalibration kitti_calibration; + vector imu_measurements; + vector gps_measurements; + loadKittiData(kitti_calibration, imu_measurements, gps_measurements); + + Vector6 BodyP = (Vector(6) << kitti_calibration.body_ptx, kitti_calibration.body_pty, kitti_calibration.body_ptz, + kitti_calibration.body_prx, kitti_calibration.body_pry, kitti_calibration.body_prz) + .finished(); + auto body_T_imu = Pose3::Expmap(BodyP); + if (!body_T_imu.equals(Pose3(), 1e-5)) { + printf("Currently only support IMUinBody is identity, i.e. IMU and body frame are the same"); + exit(-1); + } + + // Configure different variables + double t_offset = gps_measurements[0].time; + size_t first_gps_pose = 1; + size_t gps_skip = 10; // Skip this many GPS measurements each time + double g = 9.8; + auto w_coriolis = Vector3(); // zero vector + + // Configure noise models + auto noise_model_gps = noiseModel::Diagonal::Precisions((Vector(6) << Vector3::Constant(0), + Vector3::Constant(1.0/0.07)) + .finished()); + + // Set initial conditions for the estimated trajectory + // initial pose is the reference frame (navigation frame) + auto current_pose_global = Pose3(Rot3(), gps_measurements[first_gps_pose].position); + auto current_velocity_global = Vector3(); // the vehicle is stationary at the beginning at position 0,0,0 + auto current_bias = imuBias::ConstantBias(); // init with zero bias + + auto sigma_init_x = noiseModel::Diagonal::Precisions((Vector(6) << Vector3::Constant(0), + Vector3::Constant(1.0)) + .finished()); + auto sigma_init_v = noiseModel::Diagonal::Sigmas(Vector3::Constant(1000.0)); + auto sigma_init_b = noiseModel::Diagonal::Sigmas((Vector(6) << Vector3::Constant(0.100), + Vector3::Constant(5.00e-05)) + .finished()); + + // Set IMU preintegration parameters + Matrix33 measured_acc_cov = I_3x3 * pow(kitti_calibration.accelerometer_sigma, 2); + Matrix33 measured_omega_cov = I_3x3 * pow(kitti_calibration.gyroscope_sigma, 2); + // error committed in integrating position from velocities + Matrix33 integration_error_cov = I_3x3 * pow(kitti_calibration.integration_sigma, 2); + + auto imu_params = PreintegratedImuMeasurements::Params::MakeSharedU(g); + imu_params->accelerometerCovariance = measured_acc_cov; // acc white noise in continuous + imu_params->integrationCovariance = integration_error_cov; // integration uncertainty continuous + imu_params->gyroscopeCovariance = measured_omega_cov; // gyro white noise in continuous + imu_params->omegaCoriolis = w_coriolis; + + std::shared_ptr current_summarized_measurement = nullptr; + + // Set ISAM2 parameters and create ISAM2 solver object + ISAM2Params isam_params; + isam_params.factorization = ISAM2Params::CHOLESKY; + isam_params.relinearizeSkip = 10; + + ISAM2 isam(isam_params); + + // Create the factor graph and values object that will store new factors and values to add to the incremental graph + NonlinearFactorGraph new_factors; + Values new_values; // values storing the initial estimates of new nodes in the factor graph + + /// 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; + 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; + + 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>(current_pose_key, current_pose_global, sigma_init_x); + new_factors.emplace_shared>(current_vel_key, current_velocity_global, sigma_init_v); + new_factors.emplace_shared>(current_bias_key, current_bias, sigma_init_b); + } else { + double t_previous = gps_measurements[i-1].time; + + // Summarize IMU data between the previous GPS measurement and now + current_summarized_measurement = std::make_shared(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++; + } + + // 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(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((Vector(6) << + 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>(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>(current_pose_key, gps_pose, noise_model_gps); + new_values.insert(current_pose_key, gps_pose); + + printf("################ POSE INCLUDED AT TIME %lf ################\n", t); + gps_pose.translation().print(); + 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(current_pose_key); + current_velocity_global = result.at(current_vel_key); + current_bias = result.at(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"); + + 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(pose_key); + auto velocity = result.at(vel_key); + auto bias = result.at(bias_key); + + auto pose_quat = pose.rotation().toQuaternion(); + auto gps = gps_measurements[i].position; + + 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); +}