325 lines
12 KiB
C++
325 lines
12 KiB
C++
/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file ImuFactorsExample
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* @brief Test example for using GTSAM ImuFactor and ImuCombinedFactor
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* navigation code.
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* @author Garrett (ghemann@gmail.com), Luca Carlone
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*/
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/**
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* Example of use of the imuFactors (imuFactor and combinedImuFactor) in
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* conjunction with GPS
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* - imuFactor is used by default. You can test combinedImuFactor by
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* appending a `-c` flag at the end (see below for example command).
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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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* (body frame - Forward, Right, Down)
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* linAccX, linAccY, linAccZ, angVelX, angVelY, angVelX
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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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* See usage: ./ImuFactorsExample --help
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*/
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#include <boost/program_options.hpp>
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// GTSAM related includes.
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#include <gtsam/inference/Symbol.h>
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#include <gtsam/navigation/CombinedImuFactor.h>
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#include <gtsam/navigation/GPSFactor.h>
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#include <gtsam/navigation/ImuFactor.h>
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#include <gtsam/nonlinear/ISAM2.h>
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#include <gtsam/nonlinear/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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namespace po = boost::program_options;
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po::variables_map parseOptions(int argc, char* argv[]) {
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po::options_description desc;
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desc.add_options()("help,h", "produce help message")(
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"data_csv_path", po::value<string>()->default_value("imuAndGPSdata.csv"),
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"path to the CSV file with the IMU data")(
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"output_filename",
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po::value<string>()->default_value("imuFactorExampleResults.csv"),
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"path to the result file to use")("use_isam", po::bool_switch(),
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"use ISAM as the optimizer");
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po::variables_map vm;
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po::store(po::parse_command_line(argc, argv, desc), vm);
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if (vm.count("help")) {
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cout << desc << "\n";
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exit(1);
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}
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return vm;
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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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bool use_isam = false;
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po::variables_map var_map = parseOptions(argc, argv);
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data_filename = findExampleDataFile(var_map["data_csv_path"].as<string>());
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output_filename = var_map["output_filename"].as<string>();
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use_isam = var_map["use_isam"].as<bool>();
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ISAM2* isam2 = 0;
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if (use_isam) {
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printf("Using ISAM2\n");
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ISAM2Params parameters;
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parameters.relinearizeThreshold = 0.01;
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parameters.relinearizeSkip = 1;
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isam2 = new ISAM2(parameters);
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} else {
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printf("Using Levenberg Marquardt Optimizer\n");
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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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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) = stof(value.c_str());
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}
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getline(file, value, '\n');
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initial_state(9) = stof(value.c_str());
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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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Values initial_values;
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int correction_count = 0;
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initial_values.insert(X(correction_count), prior_pose);
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initial_values.insert(V(correction_count), prior_velocity);
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initial_values.insert(B(correction_count), 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 = new NonlinearFactorGraph();
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graph->addPrior(X(correction_count), prior_pose, pose_noise_model);
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graph->addPrior(V(correction_count), prior_velocity, velocity_noise_model);
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graph->addPrior(B(correction_count), prior_imu_bias, 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<PreintegratedImuMeasurements>(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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getline(file, value, ',');
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int type = stoi(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) = stof(value.c_str());
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}
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getline(file, value, '\n');
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imu(5) = stof(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) = stof(value.c_str());
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}
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getline(file, value, '\n');
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gps(6) = stof(value.c_str());
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correction_count++;
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// Adding IMU factor and GPS factor and optimizing.
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auto preint_imu =
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dynamic_cast<const PreintegratedImuMeasurements&>(*preintegrated);
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ImuFactor imu_factor(X(correction_count - 1), V(correction_count - 1),
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X(correction_count), V(correction_count),
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B(correction_count - 1), preint_imu);
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graph->add(imu_factor);
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imuBias::ConstantBias zero_bias(Vector3(0, 0, 0), Vector3(0, 0, 0));
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graph->add(BetweenFactor<imuBias::ConstantBias>(
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B(correction_count - 1), B(correction_count), zero_bias,
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bias_noise_model));
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auto correction_noise = noiseModel::Isotropic::Sigma(3, 1.0);
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GPSFactor gps_factor(X(correction_count),
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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(correction_count), prop_state.pose());
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initial_values.insert(V(correction_count), prop_state.v());
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initial_values.insert(B(correction_count), prev_bias);
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Values result;
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if (use_isam) {
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isam2->update(*graph, initial_values);
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isam2->update();
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result = isam2->calculateEstimate();
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// reset the graph
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graph->resize(0);
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initial_values.clear();
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} else {
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LevenbergMarquardtOptimizer optimizer(*graph, initial_values);
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result = optimizer.optimize();
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}
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// Overwrite the beginning of the preintegration for the next step.
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prev_state = NavState(result.at<Pose3>(X(correction_count)),
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result.at<Vector3>(V(correction_count)));
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prev_bias = result.at<imuBias::ConstantBias>(B(correction_count));
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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 gtsam_position = prev_state.pose().translation();
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Vector3 position_error = gtsam_position - gps.head<3>();
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current_position_error = position_error.norm();
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Quaternion gtsam_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 = gtsam_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";
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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, gtsam_position(0), gtsam_position(1),
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gtsam_position(2), gtsam_quat.x(), gtsam_quat.y(), gtsam_quat.z(),
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gtsam_quat.w(), gps(0), gps(1), gps(2), gps_quat.x(),
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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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