Propagator.h

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/*
 * OpenVINS: An Open Platform for Visual-Inertial Research
 * Copyright (C) 2018-2023 Patrick Geneva
 * Copyright (C) 2018-2023 Guoquan Huang
 * Copyright (C) 2018-2023 OpenVINS Contributors
 * Copyright (C) 2018-2019 Kevin Eckenhoff
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <https://www.gnu.org/licenses/>.
 */
 
#ifndef OV_MSCKF_STATE_PROPAGATOR_H
#define OV_MSCKF_STATE_PROPAGATOR_H
 
#include <atomic>
#include <memory>
#include <mutex>
 
#include "utils/sensor_data.h"
 
#include "utils/NoiseManager.h"
 
namespace ov_msckf {
 
class State;
 
class Propagator {
public:
  Propagator(NoiseManager noises, double gravity_mag) : _noises(noises), cache_imu_valid(false) {
    _noises.sigma_w_2 = std::pow(_noises.sigma_w, 2);
    _noises.sigma_a_2 = std::pow(_noises.sigma_a, 2);
    _noises.sigma_wb_2 = std::pow(_noises.sigma_wb, 2);
    _noises.sigma_ab_2 = std::pow(_noises.sigma_ab, 2);
    last_prop_time_offset = 0.0;
    _gravity << 0.0, 0.0, gravity_mag;
  }
 
  void feed_imu(const ov_core::ImuData &message, double oldest_time = -1) {
 
    // Append it to our vector
    std::lock_guard<std::mutex> lck(imu_data_mtx);
    imu_data.emplace_back(message);
 
    // Clean old measurements
    // std::cout << "PROP: imu_data.size() " << imu_data.size() << std::endl;
    clean_old_imu_measurements(oldest_time - 0.10);
  }
 
  void clean_old_imu_measurements(double oldest_time) {
    if (oldest_time < 0)
      return;
    auto it0 = imu_data.begin();
    while (it0 != imu_data.end()) {
      if (it0->timestamp < oldest_time) {
        it0 = imu_data.erase(it0);
      } else {
        it0++;
      }
    }
  }
 
  void invalidate_cache() { cache_imu_valid = false; }
 
  void propagate_and_clone(std::shared_ptr<State> state, double timestamp);
 
  bool fast_state_propagate(std::shared_ptr<State> state, double timestamp, Eigen::Matrix<double, 13, 1> &state_plus,
                            Eigen::Matrix<double, 12, 12> &covariance);
 
  static std::vector<ov_core::ImuData> select_imu_readings(const std::vector<ov_core::ImuData> &imu_data, double time0, double time1,
                                                           bool warn = true);
 
  static ov_core::ImuData interpolate_data(const ov_core::ImuData &imu_1, const ov_core::ImuData &imu_2, double timestamp) {
    // time-distance lambda
    double lambda = (timestamp - imu_1.timestamp) / (imu_2.timestamp - imu_1.timestamp);
    // PRINT_DEBUG("lambda - %d\n", lambda);
    // interpolate between the two times
    ov_core::ImuData data;
    data.timestamp = timestamp;
    data.am = (1 - lambda) * imu_1.am + lambda * imu_2.am;
    data.wm = (1 - lambda) * imu_1.wm + lambda * imu_2.wm;
    return data;
  }
 
  static Eigen::MatrixXd compute_H_Dw(std::shared_ptr<State> state, const Eigen::Vector3d &w_uncorrected);
 
  static Eigen::MatrixXd compute_H_Da(std::shared_ptr<State> state, const Eigen::Vector3d &a_uncorrected);
 
  static Eigen::MatrixXd compute_H_Tg(std::shared_ptr<State> state, const Eigen::Vector3d &a_inI);
 
protected:
  void predict_and_compute(std::shared_ptr<State> state, const ov_core::ImuData &data_minus, const ov_core::ImuData &data_plus,
                           Eigen::MatrixXd &F, Eigen::MatrixXd &Qd);
 
  void predict_mean_discrete(std::shared_ptr<State> state, double dt, const Eigen::Vector3d &w_hat, const Eigen::Vector3d &a_hat,
                             Eigen::Vector4d &new_q, Eigen::Vector3d &new_v, Eigen::Vector3d &new_p);
 
  void predict_mean_rk4(std::shared_ptr<State> state, double dt, const Eigen::Vector3d &w_hat1, const Eigen::Vector3d &a_hat1,
                        const Eigen::Vector3d &w_hat2, const Eigen::Vector3d &a_hat2, Eigen::Vector4d &new_q, Eigen::Vector3d &new_v,
                        Eigen::Vector3d &new_p);
 
  void compute_Xi_sum(std::shared_ptr<State> state, double dt, const Eigen::Vector3d &w_hat, const Eigen::Vector3d &a_hat,
                      Eigen::Matrix<double, 3, 18> &Xi_sum);
 
  void predict_mean_analytic(std::shared_ptr<State> state, double dt, const Eigen::Vector3d &w_hat, const Eigen::Vector3d &a_hat,
                             Eigen::Vector4d &new_q, Eigen::Vector3d &new_v, Eigen::Vector3d &new_p, Eigen::Matrix<double, 3, 18> &Xi_sum);
 
  void compute_F_and_G_analytic(std::shared_ptr<State> state, double dt, const Eigen::Vector3d &w_hat, const Eigen::Vector3d &a_hat,
                                const Eigen::Vector3d &w_uncorrected, const Eigen::Vector3d &a_uncorrected, const Eigen::Vector4d &new_q,
                                const Eigen::Vector3d &new_v, const Eigen::Vector3d &new_p, const Eigen::Matrix<double, 3, 18> &Xi_sum,
                                Eigen::MatrixXd &F, Eigen::MatrixXd &G);
 
  void compute_F_and_G_discrete(std::shared_ptr<State> state, double dt, const Eigen::Vector3d &w_hat, const Eigen::Vector3d &a_hat,
                                const Eigen::Vector3d &w_uncorrected, const Eigen::Vector3d &a_uncorrected, const Eigen::Vector4d &new_q,
                                const Eigen::Vector3d &new_v, const Eigen::Vector3d &new_p, Eigen::MatrixXd &F, Eigen::MatrixXd &G);
 
  NoiseManager _noises;
 
  std::vector<ov_core::ImuData> imu_data;
  std::mutex imu_data_mtx;
 
  Eigen::Vector3d _gravity;
 
  // Estimate for time offset at last propagation time
  double last_prop_time_offset = 0.0;
  bool have_last_prop_time_offset = false;
 
  // Cache of the last fast propagated state
  std::atomic<bool> cache_imu_valid;
  double cache_state_time;
  Eigen::MatrixXd cache_state_est;
  Eigen::MatrixXd cache_state_covariance;
  double cache_t_off;
};
 
} // namespace ov_msckf
 
#endif // OV_MSCKF_STATE_PROPAGATOR_H