example using CombinedImuFactor
Varun Agrawal
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file CombinedImuFactorsExample
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* @brief Test example for using GTSAM ImuCombinedFactor
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* navigation code.
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* @author Varun Agrawal
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*/
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/**
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* Example of use of the CombinedImuFactor in
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* conjunction with GPS
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* - we read IMU and GPS data from a CSV file, with the following format:
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* A row starting with "i" is the first initial position formatted with
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* N, E, D, qx, qY, qZ, qW, velN, velE, velD
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* A row starting with "0" is an imu measurement
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* linAccN, linAccE, linAccD, angVelN, angVelE, angVelD
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* A row starting with "1" is a gps correction formatted with
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* N, E, D, qX, qY, qZ, qW
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* Note that for GPS correction, we're only using the position not the
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* rotation. The rotation is provided in the file for ground truth comparison.
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*
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* Usage: ./CombinedImuFactorsExample [data_csv_path] [-c]
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* optional arguments:
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* data_csv_path path to the CSV file with the IMU data.
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* -c use CombinedImuFactor
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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/LevenbergMarquardtOptimizer.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/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 gtsam;
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using namespace std;
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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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Vector10 readInitialState(ifstream& file) {
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string value;
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// Format is (N,E,D,qX,qY,qZ,qW,velN,velE,velD)
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Vector10 initial_state;
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getline(file, value, ','); // i
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for (int i = 0; i < 9; i++) {
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getline(file, value, ',');
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initial_state(i) = atof(value.c_str());
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}
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getline(file, value, '\n');
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initial_state(9) = atof(value.c_str());
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return initial_state;
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}
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boost::shared_ptr<PreintegratedCombinedMeasurements::Params> imuParams() {
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// We use the sensor specs to build the noise model for the IMU factor.
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double accel_noise_sigma = 0.0003924;
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double gyro_noise_sigma = 0.000205689024915;
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double accel_bias_rw_sigma = 0.004905;
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double gyro_bias_rw_sigma = 0.000001454441043;
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Matrix33 measured_acc_cov = I_3x3 * pow(accel_noise_sigma, 2);
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Matrix33 measured_omega_cov = I_3x3 * pow(gyro_noise_sigma, 2);
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Matrix33 integration_error_cov =
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I_3x3 * 1e-8; // error committed in integrating position from velocities
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Matrix33 bias_acc_cov = I_3x3 * pow(accel_bias_rw_sigma, 2);
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Matrix33 bias_omega_cov = I_3x3 * pow(gyro_bias_rw_sigma, 2);
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Matrix66 bias_acc_omega_int =
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I_6x6 * 1e-5; // error in the bias used for preintegration
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auto p = PreintegratedCombinedMeasurements::Params::MakeSharedD(0.0);
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// PreintegrationBase params:
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p->accelerometerCovariance =
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measured_acc_cov; // acc white noise in continuous
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p->integrationCovariance =
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integration_error_cov; // integration uncertainty continuous
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// should be using 2nd order integration
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// PreintegratedRotation params:
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p->gyroscopeCovariance =
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measured_omega_cov; // gyro white noise in continuous
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// PreintegrationCombinedMeasurements params:
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p->biasAccCovariance = bias_acc_cov; // acc bias in continuous
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p->biasOmegaCovariance = bias_omega_cov; // gyro bias in continuous
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p->biasAccOmegaInt = bias_acc_omega_int;
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return p;
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}
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int main(int argc, char* argv[]) {
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string data_filename, output_filename;
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if (argc == 3) {
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data_filename = argv[1];
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output_filename = argv[2];
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} else if (argc == 2) {
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data_filename = argv[1];
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output_filename = "imuFactorExampleResults.csv";
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} else {
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printf("using default CSV file\n");
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data_filename = findExampleDataFile("imuAndGPSdata.csv");
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output_filename = "imuFactorExampleResults.csv";
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}
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// Set up output file for plotting errors
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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),gt_qx,"
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"gt_qy,gt_qz,gt_qw\n");
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// Begin parsing the CSV file. Input the first line for initialization.
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// From there, we'll iterate through the file and we'll preintegrate the IMU
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// or add in the GPS given the input.
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ifstream file(data_filename.c_str());
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Vector10 initial_state = readInitialState(file);
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cout << "initial state:\n" << initial_state.transpose() << "\n\n";
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// Assemble initial quaternion through GTSAM constructor
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// ::Quaternion(w,x,y,z);
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Rot3 prior_rotation = Rot3::Quaternion(initial_state(6), initial_state(3),
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initial_state(4), initial_state(5));
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Point3 prior_point(initial_state.head<3>());
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Pose3 prior_pose(prior_rotation, prior_point);
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Vector3 prior_velocity(initial_state.tail<3>());
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imuBias::ConstantBias prior_imu_bias; // assume zero initial bias
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int index = 0;
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Values initial_values;
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// insert pose at initialization
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initial_values.insert(X(index), prior_pose);
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initial_values.insert(V(index), prior_velocity);
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initial_values.insert(B(index), prior_imu_bias);
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// Assemble prior noise model and add it the graph.`
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auto pose_noise_model = noiseModel::Diagonal::Sigmas(
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(Vector(6) << 0.01, 0.01, 0.01, 0.5, 0.5, 0.5)
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.finished()); // rad,rad,rad,m, m, m
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auto velocity_noise_model = noiseModel::Isotropic::Sigma(3, 0.1); // m/s
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auto bias_noise_model = noiseModel::Isotropic::Sigma(6, 1e-3);
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// Add all prior factors (pose, velocity, bias) to the graph.
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NonlinearFactorGraph graph;
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graph.addPrior<Pose3>(X(index), prior_pose, pose_noise_model);
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graph.addPrior<Vector3>(V(index), prior_velocity, velocity_noise_model);
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graph.addPrior<imuBias::ConstantBias>(B(index), prior_imu_bias,
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bias_noise_model);
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auto p = imuParams();
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std::shared_ptr<PreintegrationType> preintegrated =
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std::make_shared<PreintegratedCombinedMeasurements>(p, prior_imu_bias);
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assert(preintegrated);
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// Store previous state for imu integration and latest predicted outcome.
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NavState prev_state(prior_pose, prior_velocity);
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NavState prop_state = prev_state;
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imuBias::ConstantBias prev_bias = prior_imu_bias;
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// Keep track of total error over the entire run as simple performance metric.
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double current_position_error = 0.0, current_orientation_error = 0.0;
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double output_time = 0.0;
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double dt = 0.005; // The real system has noise, but here, results are nearly
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// exactly the same, so keeping this for simplicity.
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// All priors have been set up, now iterate through the data file.
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while (file.good()) {
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// Parse out first value
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string value;
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getline(file, value, ',');
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int type = atoi(value.c_str());
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if (type == 0) { // IMU measurement
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Vector6 imu;
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for (int i = 0; i < 5; ++i) {
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getline(file, value, ',');
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imu(i) = atof(value.c_str());
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}
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getline(file, value, '\n');
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imu(5) = atof(value.c_str());
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// Adding the IMU preintegration.
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preintegrated->integrateMeasurement(imu.head<3>(), imu.tail<3>(), dt);
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} else if (type == 1) { // GPS measurement
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Vector7 gps;
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for (int i = 0; i < 6; ++i) {
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getline(file, value, ',');
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gps(i) = atof(value.c_str());
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}
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getline(file, value, '\n');
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gps(6) = atof(value.c_str());
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index++;
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// Adding IMU factor and GPS factor and optimizing.
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auto preint_imu_combined =
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dynamic_cast<const PreintegratedCombinedMeasurements&>(
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*preintegrated);
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CombinedImuFactor imu_factor(X(index - 1), V(index - 1), X(index),
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V(index), B(index - 1), B(index),
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preint_imu_combined);
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graph.add(imu_factor);
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auto correction_noise = noiseModel::Isotropic::Sigma(3, 1.0);
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GPSFactor gps_factor(X(index),
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Point3(gps(0), // N,
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gps(1), // E,
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gps(2)), // D,
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correction_noise);
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graph.add(gps_factor);
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// Now optimize and compare results.
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prop_state = preintegrated->predict(prev_state, prev_bias);
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initial_values.insert(X(index), prop_state.pose());
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initial_values.insert(V(index), prop_state.v());
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initial_values.insert(B(index), prev_bias);
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LevenbergMarquardtParams params;
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params.setVerbosityLM("SUMMARY");
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LevenbergMarquardtOptimizer optimizer(graph, initial_values, params);
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Values result = optimizer.optimize();
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// Overwrite the beginning of the preintegration for the next step.
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prev_state =
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NavState(result.at<Pose3>(X(index)), result.at<Vector3>(V(index)));
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prev_bias = result.at<imuBias::ConstantBias>(B(index));
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// Reset the preintegration object.
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preintegrated->resetIntegrationAndSetBias(prev_bias);
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// Print out the position and orientation error for comparison.
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Vector3 result_position = prev_state.pose().translation();
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Vector3 position_error = result_position - gps.head<3>();
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current_position_error = position_error.norm();
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Quaternion result_quat = prev_state.pose().rotation().toQuaternion();
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Quaternion gps_quat(gps(6), gps(3), gps(4), gps(5));
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Quaternion quat_error = result_quat * gps_quat.inverse();
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quat_error.normalize();
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Vector3 euler_angle_error(quat_error.x() * 2, quat_error.y() * 2,
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quat_error.z() * 2);
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current_orientation_error = euler_angle_error.norm();
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// display statistics
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cout << "Position error:" << current_position_error << "\t "
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<< "Angular error:" << current_orientation_error << "\n" << endl;
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fprintf(fp_out, "%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\n",
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output_time, result_position(0), result_position(1),
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result_position(2), result_quat.x(), result_quat.y(),
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result_quat.z(), result_quat.w(), gps(0), gps(1), gps(2),
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gps_quat.x(), gps_quat.y(), gps_quat.z(), gps_quat.w());
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output_time += 1.0;
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} else {
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cerr << "ERROR parsing file\n";
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return 1;
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}
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}
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fclose(fp_out);
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cout << "Complete, results written to " << output_filename << "\n\n";
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return 0;
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}
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