IMUKittiExampleGPS.cpp

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/* ----------------------------------------------------------------------------

 * 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

 * -------------------------------------------------------------------------- */

// GTSAM related includes.
#include <gtsam/navigation/CombinedImuFactor.h>
#include <gtsam/navigation/GPSFactor.h>
#include <gtsam/navigation/ImuFactor.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/ISAM2Params.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/inference/Symbol.h>

#include <cstring>
#include <fstream>
#include <iostream>

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<ImuMeasurement>& imu_measurements,
                   vector<GpsMeasurement>& 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<ImuMeasurement> imu_measurements;
    vector<GpsMeasurement> gps_measurements;
    loadKittiData(kitti_calibration, imu_measurements, gps_measurements);

    Vector6 BodyP = (Vector6() << 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();  // zero vector

    // Configure noise models
    auto noise_model_gps = noiseModel::Diagonal::Precisions((Vector6() << 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);
    // the vehicle is stationary at the beginning at position 0,0,0
    Vector3 current_velocity_global = Vector3::Zero();
    auto current_bias = imuBias::ConstantBias();  // init with zero bias

    auto sigma_init_x = noiseModel::Diagonal::Precisions((Vector6() << 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((Vector6() << 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<PreintegratedImuMeasurements> 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

    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<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;

            // 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++;
            }

            // 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 %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");
            }
        }
    }

    // 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<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;

        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));
    }

    fclose(fp_out);
}