408 lines
14 KiB
C++
408 lines
14 KiB
C++
#include <cmath>
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#include <math.h>
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#include <deque>
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#include <mutex>
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#include <thread>
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#include <fstream>
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#include <csignal>
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#include <ros/ros.h>
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#include <so3_math.h>
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#include <Eigen/Eigen>
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#include <common_lib.h>
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#include <pcl/common/io.h>
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#include <pcl/point_cloud.h>
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#include <pcl/point_types.h>
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#include <condition_variable>
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#include <nav_msgs/Odometry.h>
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#include <pcl/common/transforms.h>
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#include <pcl/kdtree/kdtree_flann.h>
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#include <tf/transform_broadcaster.h>
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#include <eigen_conversions/eigen_msg.h>
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#include <pcl_conversions/pcl_conversions.h>
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#include <sensor_msgs/Imu.h>
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#include <sensor_msgs/PointCloud2.h>
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#include <geometry_msgs/Vector3.h>
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#include "use-ikfom.hpp"
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#include "preprocess.h"
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/// *************Preconfiguration
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#define MAX_INI_COUNT (10)
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const bool time_list(PointType &x, PointType &y) {return (x.curvature < y.curvature);};
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/// *************IMU Process and undistortion
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class ImuProcess
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{
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public:
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EIGEN_MAKE_ALIGNED_OPERATOR_NEW
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ImuProcess();
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~ImuProcess();
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void Reset();
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void Reset(double start_timestamp, const sensor_msgs::ImuConstPtr &lastimu);
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void set_extrinsic(const V3D &transl, const M3D &rot);
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void set_extrinsic(const V3D &transl);
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void set_extrinsic(const MD(4,4) &T);
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void set_gyr_cov(const V3D &scaler);
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void set_acc_cov(const V3D &scaler);
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void set_gyr_bias_cov(const V3D &b_g);
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void set_acc_bias_cov(const V3D &b_a);
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Eigen::Matrix<double, 12, 12> Q;
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void Process(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI::Ptr pcl_un_);
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ofstream fout_imu;
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V3D cov_acc;
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V3D cov_gyr;
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V3D cov_acc_scale;
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V3D cov_gyr_scale;
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V3D cov_bias_gyr;
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V3D cov_bias_acc;
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double first_lidar_time;
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int lidar_type;
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private:
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void IMU_init(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, int &N);
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void UndistortPcl(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI &pcl_in_out);
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PointCloudXYZI::Ptr cur_pcl_un_;
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sensor_msgs::ImuConstPtr last_imu_;
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deque<sensor_msgs::ImuConstPtr> v_imu_;
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vector<Pose6D> IMUpose;
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vector<M3D> v_rot_pcl_;
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M3D Lidar_R_wrt_IMU;
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V3D Lidar_T_wrt_IMU;
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V3D mean_acc;
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V3D mean_gyr;
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V3D angvel_last;
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V3D acc_s_last;
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double start_timestamp_;
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double last_lidar_end_time_;
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int init_iter_num = 1;
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bool b_first_frame_ = true;
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bool imu_need_init_ = true;
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};
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ImuProcess::ImuProcess()
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: b_first_frame_(true), imu_need_init_(true), start_timestamp_(-1)
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{
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init_iter_num = 1;
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Q = process_noise_cov();
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cov_acc = V3D(0.1, 0.1, 0.1);
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cov_gyr = V3D(0.1, 0.1, 0.1);
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cov_bias_gyr = V3D(0.0001, 0.0001, 0.0001);
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cov_bias_acc = V3D(0.0001, 0.0001, 0.0001);
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mean_acc = V3D(0, 0, -1.0);
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mean_gyr = V3D(0, 0, 0);
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angvel_last = Zero3d;
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Lidar_T_wrt_IMU = Zero3d;
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Lidar_R_wrt_IMU = Eye3d;
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last_imu_.reset(new sensor_msgs::Imu());
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}
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ImuProcess::~ImuProcess() {}
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void ImuProcess::Reset()
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{
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// ROS_WARN("Reset ImuProcess");
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mean_acc = V3D(0, 0, -1.0);
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mean_gyr = V3D(0, 0, 0);
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angvel_last = Zero3d;
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imu_need_init_ = true;
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start_timestamp_ = -1;
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init_iter_num = 1;
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v_imu_.clear();
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IMUpose.clear();
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last_imu_.reset(new sensor_msgs::Imu());
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cur_pcl_un_.reset(new PointCloudXYZI());
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}
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void ImuProcess::set_extrinsic(const MD(4,4) &T)
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{
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Lidar_T_wrt_IMU = T.block<3,1>(0,3);
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Lidar_R_wrt_IMU = T.block<3,3>(0,0);
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}
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void ImuProcess::set_extrinsic(const V3D &transl)
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{
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Lidar_T_wrt_IMU = transl;
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Lidar_R_wrt_IMU.setIdentity();
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}
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void ImuProcess::set_extrinsic(const V3D &transl, const M3D &rot)
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{
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Lidar_T_wrt_IMU = transl;
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Lidar_R_wrt_IMU = rot;
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}
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void ImuProcess::set_gyr_cov(const V3D &scaler)
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{
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cov_gyr_scale = scaler;
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}
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void ImuProcess::set_acc_cov(const V3D &scaler)
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{
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cov_acc_scale = scaler;
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}
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void ImuProcess::set_gyr_bias_cov(const V3D &b_g)
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{
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cov_bias_gyr = b_g;
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}
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void ImuProcess::set_acc_bias_cov(const V3D &b_a)
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{
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cov_bias_acc = b_a;
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}
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void ImuProcess::IMU_init(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, int &N)
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{
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/** 1. initializing the gravity, gyro bias, acc and gyro covariance
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** 2. normalize the acceleration measurenments to unit gravity **/
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V3D cur_acc, cur_gyr;
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if (b_first_frame_)
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{
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Reset();
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N = 1;
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b_first_frame_ = false;
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const auto &imu_acc = meas.imu.front()->linear_acceleration;
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const auto &gyr_acc = meas.imu.front()->angular_velocity;
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mean_acc << imu_acc.x, imu_acc.y, imu_acc.z;
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mean_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z;
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first_lidar_time = meas.lidar_beg_time;
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}
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for (const auto &imu : meas.imu)
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{
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const auto &imu_acc = imu->linear_acceleration;
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const auto &gyr_acc = imu->angular_velocity;
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cur_acc << imu_acc.x, imu_acc.y, imu_acc.z;
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cur_gyr << gyr_acc.x, gyr_acc.y, gyr_acc.z;
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mean_acc += (cur_acc - mean_acc) / N;
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mean_gyr += (cur_gyr - mean_gyr) / N;
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cov_acc = cov_acc * (N - 1.0) / N + (cur_acc - mean_acc).cwiseProduct(cur_acc - mean_acc) * (N - 1.0) / (N * N);
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cov_gyr = cov_gyr * (N - 1.0) / N + (cur_gyr - mean_gyr).cwiseProduct(cur_gyr - mean_gyr) * (N - 1.0) / (N * N);
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// cout<<"acc norm: "<<cur_acc.norm()<<" "<<mean_acc.norm()<<endl;
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N ++;
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}
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state_ikfom init_state = kf_state.get_x();
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init_state.grav = S2(- mean_acc / mean_acc.norm() * G_m_s2);
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//state_inout.rot = Eye3d; // Exp(mean_acc.cross(V3D(0, 0, -1 / scale_gravity)));
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init_state.bg = mean_gyr;
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init_state.offset_T_L_I = Lidar_T_wrt_IMU;
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init_state.offset_R_L_I = Lidar_R_wrt_IMU;
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kf_state.change_x(init_state);
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esekfom::esekf<state_ikfom, 12, input_ikfom>::cov init_P = kf_state.get_P();
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init_P.setIdentity();
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init_P(6,6) = init_P(7,7) = init_P(8,8) = 0.00001;
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init_P(9,9) = init_P(10,10) = init_P(11,11) = 0.00001;
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init_P(15,15) = init_P(16,16) = init_P(17,17) = 0.0001;
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init_P(18,18) = init_P(19,19) = init_P(20,20) = 0.001;
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init_P(21,21) = init_P(22,22) = 0.00001;
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kf_state.change_P(init_P);
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last_imu_ = meas.imu.back();
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}
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/**
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* @brief 去畸变点云数据
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*
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* @param meas 当前处理的激光雷达和IMU数据
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* @param kf_state 扩展卡尔曼滤波器状态
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* @param pcl_out 输出的去畸变点云
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*/
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void ImuProcess::UndistortPcl(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI &pcl_out)
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{
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/*** add the imu of the last frame-tail to the of current frame-head ***/
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// 将上一帧的IMU数据添加到当前帧的IMU数据前面
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auto v_imu = meas.imu;
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v_imu.push_front(last_imu_);
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const double &imu_beg_time = v_imu.front()->header.stamp.toSec(); // 获取IMU数据的开始时间
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const double &imu_end_time = v_imu.back()->header.stamp.toSec(); // 获取IMU数据的结束时间
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double pcl_beg_time = meas.lidar_beg_time; // 获取激光雷达数据的开始时间
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double pcl_end_time = meas.lidar_end_time; // 获取激光雷达数据的结束时间
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if (lidar_type == MARSIM) {
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pcl_beg_time = last_lidar_end_time_; // 如果激光雷达类型为MARSIM，设置激光雷达开始时间为上一帧的结束时间
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pcl_end_time = meas.lidar_beg_time; // 设置激光雷达结束时间为当前帧的开始时间
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}
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/*** sort point clouds by offset time ***/
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// 按偏移时间对点云进行排序
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pcl_out = *(meas.lidar);
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sort(pcl_out.points.begin(), pcl_out.points.end(), time_list);
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// cout<<"[ IMU Process ]: Process lidar from "<<pcl_beg_time<<" to "<<pcl_end_time<<", " \
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// <<meas.imu.size()<<" imu msgs from "<<imu_beg_time<<" to "<<imu_end_time<<endl;
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/*** Initialize IMU pose ***/
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// 初始化IMU位姿
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state_ikfom imu_state = kf_state.get_x();
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IMUpose.clear();
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IMUpose.push_back(set_pose6d(0.0, acc_s_last, angvel_last, imu_state.vel, imu_state.pos, imu_state.rot.toRotationMatrix()));
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/*** forward propagation at each imu point ***/
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// 在每个IMU点进行前向传播
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V3D angvel_avr, acc_avr, acc_imu, vel_imu, pos_imu;
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M3D R_imu;
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double dt = 0;
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input_ikfom in;
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for (auto it_imu = v_imu.begin(); it_imu < (v_imu.end() - 1); it_imu++)
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{
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auto &&head = *(it_imu);
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auto &&tail = *(it_imu + 1);
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if (tail->header.stamp.toSec() < last_lidar_end_time_) continue; // 如果IMU时间小于上一帧激光雷达结束时间，跳过
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angvel_avr<<0.5 * (head->angular_velocity.x + tail->angular_velocity.x),
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0.5 * (head->angular_velocity.y + tail->angular_velocity.y),
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0.5 * (head->angular_velocity.z + tail->angular_velocity.z);
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acc_avr <<0.5 * (head->linear_acceleration.x + tail->linear_acceleration.x),
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0.5 * (head->linear_acceleration.y + tail->linear_acceleration.y),
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0.5 * (head->linear_acceleration.z + tail->linear_acceleration.z);
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// fout_imu << setw(10) << head->header.stamp.toSec() - first_lidar_time << " " << angvel_avr.transpose() << " " << acc_avr.transpose() << endl;
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acc_avr = acc_avr * G_m_s2 / mean_acc.norm(); // - state_inout.ba; // 归一化加速度
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if(head->header.stamp.toSec() < last_lidar_end_time_)
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{
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dt = tail->header.stamp.toSec() - last_lidar_end_time_; // 计算时间差
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// dt = tail->header.stamp.toSec() - pcl_beg_time;
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}
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else
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{
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dt = tail->header.stamp.toSec() - head->header.stamp.toSec(); // 计算时间差
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}
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in.acc = acc_avr;
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in.gyro = angvel_avr;
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Q.block<3, 3>(0, 0).diagonal() = cov_gyr;
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Q.block<3, 3>(3, 3).diagonal() = cov_acc;
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Q.block<3, 3>(6, 6).diagonal() = cov_bias_gyr;
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Q.block<3, 3>(9, 9).diagonal() = cov_bias_acc;
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kf_state.predict(dt, Q, in); // 预测状态
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/* save the poses at each IMU measurements */
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// 保存每个IMU测量的位姿
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imu_state = kf_state.get_x();
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angvel_last = angvel_avr - imu_state.bg;
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acc_s_last = imu_state.rot * (acc_avr - imu_state.ba);
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for(int i=0; i<3; i++)
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{
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acc_s_last[i] += imu_state.grav[i];
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}
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double &&offs_t = tail->header.stamp.toSec() - pcl_beg_time;
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IMUpose.push_back(set_pose6d(offs_t, acc_s_last, angvel_last, imu_state.vel, imu_state.pos, imu_state.rot.toRotationMatrix()));
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}
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/*** calculated the pos and attitude prediction at the frame-end ***/
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// 计算帧末端的位姿和姿态预测
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double note = pcl_end_time > imu_end_time ? 1.0 : -1.0;
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dt = note * (pcl_end_time - imu_end_time);
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kf_state.predict(dt, Q, in);
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imu_state = kf_state.get_x();
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last_imu_ = meas.imu.back();
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last_lidar_end_time_ = pcl_end_time;
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/*** undistort each lidar point (backward propagation) ***/
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// 去畸变每个激光雷达点（反向传播）
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if (pcl_out.points.begin() == pcl_out.points.end()) return;
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if(lidar_type != MARSIM){
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auto it_pcl = pcl_out.points.end() - 1;
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for (auto it_kp = IMUpose.end() - 1; it_kp != IMUpose.begin(); it_kp--)
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{
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auto head = it_kp - 1;
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auto tail = it_kp;
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R_imu<<MAT_FROM_ARRAY(head->rot);
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// cout<<"head imu acc: "<<acc_imu.transpose()<<endl;
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vel_imu<<VEC_FROM_ARRAY(head->vel);
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pos_imu<<VEC_FROM_ARRAY(head->pos);
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acc_imu<<VEC_FROM_ARRAY(tail->acc);
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angvel_avr<<VEC_FROM_ARRAY(tail->gyr);
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// 去畸变每个激光雷达点（反向传播）
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for(; it_pcl->curvature / double(1000) > head->offset_time; it_pcl --)
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{
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dt = it_pcl->curvature / double(1000) - head->offset_time;
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/* Transform to the 'end' frame, using only the rotation
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* Note: Compensation direction is INVERSE of Frame's moving direction
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* So if we want to compensate a point at timestamp-i to the frame-e
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* P_compensate = R_imu_e ^ T * (R_i * P_i + T_ei) where T_ei is represented in global frame */
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// 将点转换到“结束”帧，仅使用旋转
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M3D R_i(R_imu * Exp(angvel_avr, dt));
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V3D P_i(it_pcl->x, it_pcl->y, it_pcl->z);
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V3D T_ei(pos_imu + vel_imu * dt + 0.5 * acc_imu * dt * dt - imu_state.pos);
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V3D P_compensate = imu_state.offset_R_L_I.conjugate() * (imu_state.rot.conjugate() * (R_i * (imu_state.offset_R_L_I * P_i + imu_state.offset_T_L_I) + T_ei) - imu_state.offset_T_L_I);// not accurate!
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// save Undistorted points and their rotation
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// 保存去畸变后的点和它们的旋转
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it_pcl->x = P_compensate(0);
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it_pcl->y = P_compensate(1);
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it_pcl->z = P_compensate(2);
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if (it_pcl == pcl_out.points.begin()) break;
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}
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}
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}
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}
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void ImuProcess::Process(const MeasureGroup &meas, esekfom::esekf<state_ikfom, 12, input_ikfom> &kf_state, PointCloudXYZI::Ptr cur_pcl_un_)
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{
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double t1,t2,t3;
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t1 = omp_get_wtime(); // 获取当前时间
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if(meas.imu.empty()) {return;}; // 如果IMU数据为空，直接返回
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ROS_ASSERT(meas.lidar != nullptr); // 断言激光雷达数据不为空
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if (imu_need_init_)
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{
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// 如果需要初始化IMU
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/// The very first lidar frame
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IMU_init(meas, kf_state, init_iter_num); // 初始化IMU
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imu_need_init_ = true; // 设置IMU需要初始化标志为真
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last_imu_ = meas.imu.back(); // 获取最后一个IMU数据
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state_ikfom imu_state = kf_state.get_x(); // 获取当前状态
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if (init_iter_num > MAX_INI_COUNT)
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{
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// 如果初始化迭代次数大于最大初始化次数
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cov_acc *= pow(G_m_s2 / mean_acc.norm(), 2); // 调整加速度协方差
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imu_need_init_ = false; // 设置IMU需要初始化标志为假
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cov_acc = cov_acc_scale; // 设置加速度协方差
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cov_gyr = cov_gyr_scale; // 设置陀螺仪协方差
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ROS_INFO("IMU Initial Done"); // 打印IMU初始化完成信息
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// ROS_INFO("IMU Initial Done: Gravity: %.4f %.4f %.4f %.4f; state.bias_g: %.4f %.4f %.4f; acc covarience: %.8f %.8f %.8f; gry covarience: %.8f %.8f %.8f",\
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// imu_state.grav[0], imu_state.grav[1], imu_state.grav[2], mean_acc.norm(), cov_bias_gyr[0], cov_bias_gyr[1], cov_bias_gyr[2], cov_acc[0], cov_acc[1], cov_acc[2], cov_gyr[0], cov_gyr[1], cov_gyr[2]);
|
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fout_imu.open(DEBUG_FILE_DIR("imu.txt"),ios::out); // 打开IMU调试文件
|
||
}
|
||
|
||
return;
|
||
}
|
||
|
||
UndistortPcl(meas, kf_state, *cur_pcl_un_); // 去畸变点云
|
||
|
||
t2 = omp_get_wtime();
|
||
t3 = omp_get_wtime();
|
||
|
||
// cout<<"[ IMU Process ]: Time: "<<t3 - t1<<endl;
|
||
}
|