代码浅析Point-LIO_pointlio与fastlio

0. 简介

对于最近出来的Point-LIO(鲁棒高带宽激光惯性里程计)，本人还是非常该兴趣的，为此花了一些时间重点分析了Point-LIO的代码，并研究了它相较于Fast-LIO2的区别

1. laserMapping.cpp

第一部分就是实现对激光雷达视场角的图像分割。首先定义了一个BoxPointType类型的局部地图(LocalMap_Points)和一个bool类型的变量(Localmap_Initialized)，表示是否已经初始化局部地图。然后，在lasermap_fov_segment()函数中，根据激光雷达的姿态计算出激光雷达的位置(pos_LiD)，并根据移动阈值(MOV_THRESHOLD)判断是否需要移动局部地图。如果需要移动，则计算新的局部地图边界(New_LocalMap_Points)，并将需要移除的框添加到cub_needrm中。最后，根据cub_needrm中的框删除点云，完成图像分割。

BoxPointType LocalMap_Points;
bool Localmap_Initialized = false;
void lasermap_fov_segment() //针对激光雷达视场角来完成图像分割
{
  cub_needrm.shrink_to_fit(); //将容量设置为容器的长度
 
  V3D pos_LiD;
  if (use_imu_as_input) {
    pos_LiD =
        kf_input.x_.pos + kf_input.x_.rot.normalized() *
                              Lidar_T_wrt_IMU; //计算激光雷达在当前位姿下的位置
  } else {
    pos_LiD =
        kf_output.x_.pos + kf_output.x_.rot.normalized() * Lidar_T_wrt_IMU;
  }
  if (!Localmap_Initialized) { //判断是否需要初始化局部地图
    //将局部地图的边界设置为当前位置的正方形区域，并将Localmap_Initialized设置为true
    for (int i = 0; i < 3; i++) {
      LocalMap_Points.vertex_min[i] = pos_LiD(i) - cube_len / 2.0;
      LocalMap_Points.vertex_max[i] = pos_LiD(i) + cube_len / 2.0;
    }
    Localmap_Initialized = true;
    return;
  }
  float dist_to_map_edge[3][2];
  bool need_move = false;
  //如果不需要初始化，则计算激光雷达当前位姿与局部地图边界的距离，并根据移动阈值判断是否需要移动局部地图
  for (int i = 0; i < 3; i++) {
    dist_to_map_edge[i][0] = fabs(pos_LiD(i) - LocalMap_Points.vertex_min[i]);
    dist_to_map_edge[i][1] = fabs(pos_LiD(i) - LocalMap_Points.vertex_max[i]);
    if (dist_to_map_edge[i][0] <= MOV_THRESHOLD * DET_RANGE ||
        dist_to_map_edge[i][1] <= MOV_THRESHOLD * DET_RANGE)
      need_move = true;
  }
  //如果需要移动，则计算新的局部地图边界
  if (!need_move)
    return;
  BoxPointType New_LocalMap_Points, tmp_boxpoints;
  New_LocalMap_Points = LocalMap_Points;
  float mov_dist = max((cube_len - 2.0 * MOV_THRESHOLD * DET_RANGE) * 0.5 * 0.9,
                       double(DET_RANGE * (MOV_THRESHOLD - 1)));
  for (int i = 0; i < 3; i++) {
    tmp_boxpoints = LocalMap_Points;
    if (dist_to_map_edge[i][0] <= MOV_THRESHOLD * DET_RANGE) {
      New_LocalMap_Points.vertex_max[i] -= mov_dist;
      New_LocalMap_Points.vertex_min[i] -= mov_dist;
      tmp_boxpoints.vertex_min[i] = LocalMap_Points.vertex_max[i] - mov_dist;
      cub_needrm.emplace_back(tmp_boxpoints); //将需要移除的框添加到cub_needrm中
    } else if (dist_to_map_edge[i][1] <= MOV_THRESHOLD * DET_RANGE) {
      New_LocalMap_Points.vertex_max[i] += mov_dist;
      New_LocalMap_Points.vertex_min[i] += mov_dist;
      tmp_boxpoints.vertex_max[i] = LocalMap_Points.vertex_min[i] + mov_dist;
      cub_needrm.emplace_back(tmp_boxpoints);
    }
  }
  LocalMap_Points = New_LocalMap_Points;
 
  points_cache_collect();
  if (cub_needrm.size() > 0)
    int kdtree_delete_counter = ikdtree.Delete_Point_Boxes(
        cub_needrm); //收集点云缓存，并根据cub_needrm中的框删除点云，返回删除的框数量。
}

下面我们来看看怎么使用这个函数的，下面的代码主要实现了以下的操作：

1.对激光雷达采集到的点云进行空间下采样和时间压缩；

2.初始化地图kd-tree；

3.使用迭代最近点算法（ICP）和卡尔曼滤波更新地图。其中，ICP主要用于点云配准，卡尔曼滤波则用于对机器人位姿进行估计和更新。

lasermap_fov_segment();
      /*** downsample the feature points in a scan ***/
      t1 = omp_get_wtime();
      if (space_down_sample) { //空间下采样
        downSizeFilterSurf.setInputCloud(feats_undistort);
        downSizeFilterSurf.filter(*feats_down_body);
        sort(feats_down_body->points.begin(), feats_down_body->points.end(),
             time_list); //按时间排序
      } else {
        feats_down_body = Measures.lidar;
        sort(feats_down_body->points.begin(), feats_down_body->points.end(),
             time_list);
      }
      time_seq = time_compressing<int>(feats_down_body); //时间压缩
      feats_down_size = feats_down_body->points.size();  //点云数量
 
      /*** initialize the map kdtree ***/
      if (!init_map) {
        if (ikdtree.Root_Node == nullptr) //
        // if(feats_down_size > 5)
        {
          ikdtree.set_downsample_param(filter_size_map_min); //设置滤波参数
        }
 
        feats_down_world->resize(feats_down_size); //初始化点云
        for (int i = 0; i < feats_down_size; i++) {
          pointBodyToWorld(&(feats_down_body->points[i]),
                           &(feats_down_world->points[i])); //转换到世界坐标系
        }
        for (size_t i = 0; i < feats_down_world->size(); i++) {
          init_feats_world->points.emplace_back(
              feats_down_world
                  ->points[i]); //将转换到世界坐标西的点云加入到init_feats_world
        }
        if (init_feats_world->size() < init_map_size) //等待构建地图
          continue;
        ikdtree.Build(init_feats_world->points); //构建地图
        init_map = true;
        publish_init_kdtree(pubLaserCloudMap); //(pubLaserCloudFullRes);
        continue;
      }
      /*** ICP and Kalman filter update ***/
      normvec->resize(feats_down_size);
      feats_down_world->resize(feats_down_size);
 
      Nearest_Points.resize(feats_down_size);
 
      t2 = omp_get_wtime(); //初始化t2为当前时间
 
      /*** iterated state estimation ***/
      crossmat_list.reserve(feats_down_size);
      pbody_list.reserve(feats_down_size);
      // pbody_ext_list.reserve(feats_down_size);
 
      //对于每个点，将其坐标转换为V3D类型的point_this
      for (size_t i = 0; i < feats_down_body->size(); i++) {
        V3D point_this(feats_down_body->points[i].x,
                       feats_down_body->points[i].y,
                       feats_down_body->points[i].z);
        pbody_list[i] = point_this;
        //如果使用外参估计
        if (extrinsic_est_en) {
          if (!use_imu_as_input) {
            //对于每个点，使用卡尔曼滤波估计出的外参对其进行坐标变换
            point_this = kf_output.x_.offset_R_L_I.normalized() * point_this +
                         kf_output.x_.offset_T_L_I;
          } else {
            point_this = kf_input.x_.offset_R_L_I.normalized() * point_this +
                         kf_input.x_.offset_T_L_I;
          }
        } else {
          // 使用Lidar_R_wrt_IMU和Lidar_T_wrt_IMU对其进行变换
          point_this = Lidar_R_wrt_IMU * point_this + Lidar_T_wrt_IMU;
        }
        M3D point_crossmat;
        point_crossmat << SKEW_SYM_MATRX(point_this); //根据当前点生成矩阵
        crossmat_list[i] = point_crossmat;
      }
 
      if (!use_imu_as_input) {
        bool imu_upda_cov = false; //是否需要更新imu的协方差
        effct_feat_num = 0;
        /**** point by point update ****/
 
        double pcl_beg_time =
            Measures
                .lidar_beg_time; //首先设置pcl_beg_time为Measures.lidar_beg_time，idx为-1
        idx = -1;
        for (k = 0; k < time_seq.size(); k++) {
          PointType &point_body = feats_down_body->points[idx + time_seq[k]];
 
          time_current =
              point_body.curvature / 1000.0 +
              pcl_beg_time; //找到对应的点并计算出当前时间time_current
 
          if (is_first_frame) {
            if (imu_en) { //如果是第一帧，且启用了IMU，那么需要找到最近的IMU数据
              while (time_current > imu_next.header.stamp.toSec()) {
                imu_last = imu_next;
                imu_next = *(imu_deque.front());
                imu_deque.pop_front();
                // imu_deque.pop();
              }
              //计算出对应的角速度和加速度
              angvel_avr << imu_last.angular_velocity.x,
                  imu_last.angular_velocity.y, imu_last.angular_velocity.z;
              acc_avr << imu_last.linear_acceleration.x,
                  imu_last.linear_acceleration.y,
                  imu_last.linear_acceleration.z;
            }
            is_first_frame = false;
            imu_upda_cov = true;
            time_update_last = time_current;
            time_predict_last_const = time_current;
          }
          if (imu_en) {
            bool imu_comes = time_current > imu_next.header.stamp.toSec();
            // 如果启用了IMU，那么需要在当前时间之前的IMU数据都进行卡尔曼滤波更新
            while (imu_comes) {
              imu_upda_cov = true;
              //将IMU数据中的角速度和线性加速度分别存储到angvel_avr和acc_avr中
              angvel_avr << imu_next.angular_velocity.x,
                  imu_next.angular_velocity.y, imu_next.angular_velocity.z;
              acc_avr << imu_next.linear_acceleration.x,
                  imu_next.linear_acceleration.y,
                  imu_next.linear_acceleration.z;
 
              /*** 对协方差进行更新 ***/
              imu_last = imu_next; //将当前IMU数据存储为imu_last
              imu_next = *(imu_deque.front()); //将下一个IMU数据存储为imu_next
              imu_deque.pop_front();
              double dt = imu_last.header.stamp.toSec() -
                          time_predict_last_const; //接着计算时间差dt
              kf_output.predict(dt, Q_output, input_in, true,
                                false); //通过kf_output.predict函数进行预测
              time_predict_last_const =
                  imu_last.header.stamp.toSec(); // big problem
              imu_comes = time_current > imu_next.header.stamp.toSec();
              // if (!imu_comes)
              {
                double dt_cov = imu_last.header.stamp.toSec() -
                                time_update_last; //就计算时间差dt_cov
 
                if (dt_cov > 0.0) {
                  time_update_last = imu_last.header.stamp.toSec();
                  double propag_imu_start = omp_get_wtime();
 
                  kf_output.predict(dt_cov, Q_output, input_in, false,
                                    true); //行卡尔曼滤波预测
 
                  propag_time += omp_get_wtime() - propag_imu_start;
                  double solve_imu_start = omp_get_wtime();
                  kf_output.update_iterated_dyn_share_IMU(); //进行eskf迭代更新
                  solve_time += omp_get_wtime() - solve_imu_start;
                }
              }
            }
          }
 
          double dt = time_current - time_predict_last_const;
          double propag_state_start = omp_get_wtime();
          if (!prop_at_freq_of_imu) {
            double dt_cov = time_current - time_update_last;
            if (dt_cov > 0.0) {
              kf_output.predict(dt_cov, Q_output, input_in, false, true);
              time_update_last = time_current;
            }
          }
          kf_output.predict(dt, Q_output, input_in, true, false);
          propag_time += omp_get_wtime() - propag_state_start;
          time_predict_last_const = time_current;
          // if(k == 0)
          // {
          // fout_imu_pbp << Measures.lidar_last_time - first_lidar_time <<
          // " " << imu_last.angular_velocity.x << " " <<
          // imu_last.angular_velocity.y << " " <<
          // imu_last.angular_velocity.z \ // << " " << imu_last.linear_acceleration.x << " " <<
          // imu_last.linear_acceleration.y << " " <<
          // imu_last.linear_acceleration.z << endl;
          // }
 
          double t_update_start = omp_get_wtime();
 
          if (feats_down_size < 1) {
            ROS_WARN("No point, skip this scan!\n");
            idx += time_seq[k];
            continue;
          }
          if (!kf_output.update_iterated_dyn_share_modified()) {
            idx = idx + time_seq[k];
            continue;
          }
 
          if (prop_at_freq_of_imu) {
            double dt_cov = time_current - time_update_last;
            if (!imu_en &&
                (dt_cov >=
                 imu_time_inte)) // //需要在当前时间之前的时间间隔大于imu_time_inte时进行卡尔曼滤波协方差更新
            {
              double propag_cov_start = omp_get_wtime();
              kf_output.predict(
                  dt_cov, Q_output, input_in, false,
                  true); //对于每个时间片中的点，进行卡尔曼滤波更新，并将点转换到世界坐标系中
              imu_upda_cov = false;
              time_update_last = time_current;
              propag_time += omp_get_wtime() - propag_cov_start;
            }
          }
 
          solve_start = omp_get_wtime();

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