EGO-Swarm代码解读-地图部分

1、参数解读

一、MappingData md_中的参数含义：

local_bound_min_, local_bound_max_ //更新栅格的范围

  //具体占据概率,初始化为-1.99243-0.01=-2.00243(空闲)md_.occupancy_buffer_ = vector<double>(buffer_size, mp_.clamp_min_log_ - mp_.unknown_flag_);//栅格占据与否,0表示空闲,1表示占据md_.occupancy_buffer_inflate_ = vector<char>(buffer_size, 0);
//统计涉及到的体素的次数,占据和空闲均会+1md_.count_hit_and_miss_ = vector<short>(buffer_size, 0);
//统计涉及到的占据体素的次数,占据时会+1md_.count_hit_ = vector<short>(buffer_size, 0);md_.flag_rayend_ = vector<char>(buffer_size, -1);md_.flag_traverse_ = vector<char>(buffer_size, -1);md_.raycast_num_ = 0;md_.proj_points_.resize(640 * 480 / mp_.skip_pixel_ / mp_.skip_pixel_);md_.proj_points_cnt = 0;md_.cam2body_ << 0.0, 0.0, 1.0, 0.0,-1.0, 0.0, 0.0, 0.0,0.0, -1.0, 0.0, 0.0,0.0, 0.0, 0.0, 1.0;

二、MappingParameters mp_中的参数含义：

//表示栅格地图的索引（index）范围,mp_.resolution_表示地图分表率，默认值为0.1;ceil向上取整for (int i = 0; i < 3; ++i){mp_.map_voxel_num_(i) = ceil(mp_.map_size_(i) / mp_.resolution_);}
//缓冲区大小（三维索引 ---> 一维向量）
int buffer_size = mp_.map_voxel_num_(0) * mp_.map_voxel_num_(1) * mp_.map_voxel_num_(2);

//表示地图的位置（pos）范围mp_.map_min_boundary_ = mp_.map_origin_;mp_.map_max_boundary_ = mp_.map_origin_ + mp_.map_size_;

mp_.local_map_margin_//表示局部栅格地图的更新和清除范围,默认值为10(10*0.1=1m)

  mp_.prob_hit_log_ = logit(mp_.p_hit_);//每判定一次占据,自增mp_.prob_hit_log_ mp_.prob_miss_log_ = logit(mp_.p_miss_);//每判定一次空闲,自增mp_.prob_miss_log_ mp_.clamp_min_log_ = logit(mp_.p_min_);//最终能够判定空闲的阈值mp_.clamp_max_log_ = logit(mp_.p_max_);//最终能够判定占据的阈值mp_.min_occupancy_log_ = logit(mp_.p_occ_);mp_.unknown_flag_ = 0.01;cout << "hit: " << mp_.prob_hit_log_ << endl;cout << "miss: " << mp_.prob_miss_log_ << endl;cout << "min log: " << mp_.clamp_min_log_ << endl;cout << "max: " << mp_.clamp_max_log_ << endl;cout << "thresh log: " << mp_.min_occupancy_log_ << endl;//终端输出结果hit: 0.619039miss: -0.619039min log: -1.99243max: 2.19722thresh log: 1.38629

三、grid_map.cpp文件中其他参数含义：

x_size, y_size, z_size//代表设定地图的大小
mp_.map_size_ = Eigen::Vector3d(x_size, y_size, z_size);

//表示地图的原点在地图的左下角，mp_.ground_height_默认值为-0.01
mp_.map_origin_ = Eigen::Vector3d(-x_size / 2.0, -y_size / 2.0, mp_.ground_height_);

2、主要函数解读

1. indep_cloud_sub_ =
node_.subscribe<sensor_msgs::PointCloud2>(“grid_map/cloud”, 10, &GridMap::cloudCallback, this);

void GridMap::cloudCallback(const sensor_msgs::PointCloud2ConstPtr &img)
{// double t_cur = img->header.stamp.toSec();// printf("cloudCallback_time: %16f\n",t_cur);pcl::PointCloud<pcl::PointXYZ> latest_cloud;pcl::fromROSMsg(*img, latest_cloud);//将ROS点云消息类型转换为PCL标准库中点云消息类型md_.has_cloud_ = true;if (!md_.has_odom_){std::cout << "no odom!" << std::endl;return;}if (latest_cloud.points.size() == 0)return;if (isnan(md_.camera_pos_(0)) || isnan(md_.camera_pos_(1)) || isnan(md_.camera_pos_(2)))return;//md_.occupancy_buffer_inflate_[pos] = 0;this->resetBuffer(md_.camera_pos_ - mp_.local_update_range_,md_.camera_pos_ + mp_.local_update_range_);pcl::PointXYZ pt;Eigen::Vector3d p3d, p3d_inf;//obstacles_inflation_=0.099,resolution_=0.1,ceil向上取整，inf_step=1.0int inf_step = ceil(mp_.obstacles_inflation_ / mp_.resolution_);int inf_step_z = 1;double max_x, max_y, max_z, min_x, min_y, min_z;//map_max_boundary,map_min_boundary表示地图的位置（pos）范围min_x = mp_.map_max_boundary_(0);min_y = mp_.map_max_boundary_(1);min_z = mp_.map_max_boundary_(2);max_x = mp_.map_min_boundary_(0);max_y = mp_.map_min_boundary_(1);max_z = mp_.map_min_boundary_(2);for (size_t i = 0; i < latest_cloud.points.size(); ++i){pt = latest_cloud.points[i];p3d(0) = pt.x, p3d(1) = pt.y, p3d(2) = pt.z;/* point inside update range */Eigen::Vector3d devi = p3d - md_.camera_pos_;Eigen::Vector3i inf_pt;//mp_.local_update_range_x=5.5,y=5.5,z=4.5if (fabs(devi(0)) < mp_.local_update_range_(0) && fabs(devi(1)) < mp_.local_update_range_(1) &&fabs(devi(2)) < mp_.local_update_range_(2)){/* inflate the point */for (int x = -inf_step; x <= inf_step; ++x)for (int y = -inf_step; y <= inf_step; ++y)for (int z = -inf_step_z; z <= inf_step_z; ++z){p3d_inf(0) = pt.x + x * mp_.resolution_;p3d_inf(1) = pt.y + y * mp_.resolution_;p3d_inf(2) = pt.z + z * mp_.resolution_;//第一次进入循环:相当于max_x = p3d_inf(0)max_x = max(max_x, p3d_inf(0));max_y = max(max_y, p3d_inf(1));max_z = max(max_z, p3d_inf(2));
//第一次进入循环:膨胀后的点云的位置与地图最大位置边界---二者取最小(相当于min_x = p3d_inf(0))min_x = min(min_x, p3d_inf(0));min_y = min(min_y, p3d_inf(1));min_z = min(min_z, p3d_inf(2));posToIndex(p3d_inf, inf_pt);if (!isInMap(inf_pt))continue;int idx_inf = toAddress(inf_pt);//膨胀之后的占据栅格，一个占据点云左右各膨胀0.1(一个栅格)md_.occupancy_buffer_inflate_[idx_inf] = 1;}}//三层for循环结束后,max_x的值代表膨胀后的点云的最大位置坐标,//min_x的值代表膨胀后的点云的最小位置坐标}
//比较膨胀后的点云的最小位置 和 相机的位置, min_x取二者的最小值min_x = min(min_x, md_.camera_pos_(0));min_y = min(min_y, md_.camera_pos_(1));min_z = min(min_z, md_.camera_pos_(2));
//比较膨胀后的点云的最大位置 和 相机的位置, max_x取二者的最大值max_x = max(max_x, md_.camera_pos_(0));max_y = max(max_y, md_.camera_pos_(1));max_z = max(max_z, md_.camera_pos_(2));max_z = max(max_z, mp_.ground_height_);
//重置更新栅格的范围posToIndex(Eigen::Vector3d(max_x, max_y, max_z), md_.local_bound_max_);posToIndex(Eigen::Vector3d(min_x, min_y, min_z), md_.local_bound_min_);
//确保待更新栅格均在地图内.若不在,取地图边界值予以替代boundIndex(md_.local_bound_min_);boundIndex(md_.local_bound_max_);// add virtual ceiling to limit flight height/*mp_.virtual_ceil_height_默认值为3.0*/if (mp_.virtual_ceil_height_ > -0.5) {//floor向下取整/*mp_.map_origin_ = Eigen::Vector3d(-x_size / 2.0, -y_size / 2.0, mp_.ground_height_);*///mp_.resolution_inv_ = 1/0.1= 10//ceil_id = 30int ceil_id = floor((mp_.virtual_ceil_height_ - mp_.map_origin_(2)) * mp_.resolution_inv_);/*local_bound_min_ 表示 占据栅格更新范围.一个占据栅格0.1m,将障碍物最高设置为30*0.1=3m,防止飞机从障碍物上方飞过*/for (int x = md_.local_bound_min_(0); x <= md_.local_bound_max_(0); ++x)for (int y = md_.local_bound_min_(1); y <= md_.local_bound_max_(1); ++y) {md_.occupancy_buffer_inflate_[toAddress(x, y, ceil_id)] = 1;}}
}

void GridMap::resetBuffer(Eigen::Vector3d min_pos, Eigen::Vector3d max_pos)
{Eigen::Vector3i min_id, max_id;//将位置转换为索引posToIndex(min_pos, min_id);posToIndex(max_pos, max_id);//确保最小索引min_id,最大索引max_id均在地图内boundIndex(min_id);boundIndex(max_id);/* reset occ and dist buffer */for (int x = min_id(0); x <= max_id(0); ++x)for (int y = min_id(1); y <= max_id(1); ++y)for (int z = min_id(2); z <= max_id(2); ++z){//空闲md_.occupancy_buffer_inflate_[toAddress(x, y, z)] = 0;}
}

inline void GridMap::boundIndex(Eigen::Vector3i& id) {Eigen::Vector3i id1;id1(0) = max(min(id(0), mp_.map_voxel_num_(0) - 1), 0);id1(1) = max(min(id(1), mp_.map_voxel_num_(1) - 1), 0);id1(2) = max(min(id(2), mp_.map_voxel_num_(2) - 1), 0);id = id1;
}

//三维向量 ---> 一维向量
inline int GridMap::toAddress(int& x, int& y, int& z) {return x * mp_.map_voxel_num_(1) * mp_.map_voxel_num_(2) + y * mp_.map_voxel_num_(2) + z;
}

2. void GridMap::raycastProcess()

void GridMap::raycastProcess()
{// if (md_.proj_points_.size() == 0)if (md_.proj_points_cnt == 0)return;ros::Time t1, t2;//md_.raycast_num_初始化为0md_.raycast_num_ += 1;int vox_idx;double length;// bounding box of updated region//map_max_boundary,map_min_boundary表示地图的位置（pos）范围double min_x = mp_.map_max_boundary_(0);double min_y = mp_.map_max_boundary_(1);double min_z = mp_.map_max_boundary_(2);double max_x = mp_.map_min_boundary_(0);double max_y = mp_.map_min_boundary_(1);double max_z = mp_.map_min_boundary_(2);RayCaster raycaster;Eigen::Vector3d half = Eigen::Vector3d(0.5, 0.5, 0.5);Eigen::Vector3d ray_pt, pt_w;for (int i = 0; i < md_.proj_points_cnt; ++i){pt_w = md_.proj_points_[i];// set flag for projected point/*判断该点是否在地图内*//*若该点不在地图内,则在地图范围内找一个与该点靠近的点；又若新找到的点不在射线追踪范围,依旧在找一个在射线追踪范围内的新的点.最终设为**空闲***/if (!isInMap(pt_w)){pt_w = closetPointInMap(pt_w, md_.camera_pos_);length = (pt_w - md_.camera_pos_).norm();if (length > mp_.max_ray_length_)//mp_.max_ray_length_ = 4.5{pt_w = (pt_w - md_.camera_pos_) / length * mp_.max_ray_length_ + md_.camera_pos_;}vox_idx = setCacheOccupancy(pt_w, 0);}/* 若该点在地图内:1.不在射线追踪范围,依据该点找一个在射线追踪范围内的新的点,设为**空闲** 2.在射线追踪范围,设为**占据** */else{length = (pt_w - md_.camera_pos_).norm();if (length > mp_.max_ray_length_){pt_w = (pt_w - md_.camera_pos_) / length * mp_.max_ray_length_ + md_.camera_pos_;vox_idx = setCacheOccupancy(pt_w, 0);}else{vox_idx = setCacheOccupancy(pt_w, 1);}}
//第一次进入for循环,相当于max_x = pt_w(0),pt_w是点云的位置坐标.之后是取待追踪点云的最小位置坐标max_x = max(max_x, pt_w(0));max_y = max(max_y, pt_w(1));max_z = max(max_z, pt_w(2));
//第一次进入for循环,相当于min_x = pt_w(0).之后是取待追踪点云的最大位置坐标min_x = min(min_x, pt_w(0));min_y = min(min_y, pt_w(1));min_z = min(min_z, pt_w(2));// raycasting between camera center and point//vox_idx是个一维向量,即是一个数//enum { POSE_STAMPED = 1, ODOMETRY = 2, INVALID_IDX = -10000 };/*if (occ != 1 && occ != 0)return INVALID_IDX;*/if (vox_idx != INVALID_IDX){// md_.flag_rayend_ = vector<char>(buffer_size, -1);初始化为-1//确保一个vox_idx只塞进md_.flag_rayend_一次,用于提高效率if (md_.flag_rayend_[vox_idx] == md_.raycast_num_){continue;}else{md_.flag_rayend_[vox_idx] = md_.raycast_num_;}}
/*pt_w / mp_.resolution_表示点云的栅格坐标, 
md_.camera_pos_ / mp_.resolution_表示相机的栅格坐标*/
//raycaster.setInput的具体实现见raycast.cpp文件raycaster.setInput(pt_w / mp_.resolution_, md_.camera_pos_ / mp_.resolution_);
/*   raycaster.step(ray_pt)=false表示射线追踪到了最后一个点,
即md_.camera_pos_ / mp_.resolution_ .ray_pt表示当前射线追踪点的栅格坐标  */while (raycaster.step(ray_pt)){//tmp表示当前射线追踪点的位置坐标 Eigen::Vector3d tmp = (ray_pt + half) * mp_.resolution_;length = (tmp - md_.camera_pos_).norm();// if (length < mp_.min_ray_length_) break;vox_idx = setCacheOccupancy(tmp, 0);if (vox_idx != INVALID_IDX){if (md_.flag_traverse_[vox_idx] == md_.raycast_num_){break;}else{md_.flag_traverse_[vox_idx] = md_.raycast_num_;}}}}//比较点云的最小位置 和 相机的位置, min_x取二者的最小值min_x = min(min_x, md_.camera_pos_(0));min_y = min(min_y, md_.camera_pos_(1));min_z = min(min_z, md_.camera_pos_(2));//比较点云的最大位置 和 相机的位置, min_x取二者的最大值max_x = max(max_x, md_.camera_pos_(0));max_y = max(max_y, md_.camera_pos_(1));max_z = max(max_z, md_.camera_pos_(2));max_z = max(max_z, mp_.ground_height_);//将位置坐标转换为栅格坐标,重置更新栅格的范围posToIndex(Eigen::Vector3d(max_x, max_y, max_z), md_.local_bound_max_);posToIndex(Eigen::Vector3d(min_x, min_y, min_z), md_.local_bound_min_);//确保待更新栅格均在地图内.若不在,取地图边界值予以替代boundIndex(md_.local_bound_min_);boundIndex(md_.local_bound_max_);/*if (md_.local_updated_){clearAndInflateLocalMap();}*/md_.local_updated_ = true;// update occupancy cached(已缓存) in queueEigen::Vector3d local_range_min = md_.camera_pos_ - mp_.local_update_range_;Eigen::Vector3d local_range_max = md_.camera_pos_ + mp_.local_update_range_;Eigen::Vector3i min_id, max_id;posToIndex(local_range_min, min_id);posToIndex(local_range_max, max_id);boundIndex(min_id);boundIndex(max_id);// std::cout << "cache all: " << md_.cache_voxel_.size() << std::endl;while (!md_.cache_voxel_.empty()){Eigen::Vector3i idx = md_.cache_voxel_.front();int idx_ctns = toAddress(idx);md_.cache_voxel_.pop();
//若判定占据,log_odds_update =mp_.prob_hit_log_;若判定空闲,log_odds_update =mp_.prob_miss_log_double log_odds_update =md_.count_hit_[idx_ctns] >= md_.count_hit_and_miss_[idx_ctns] - md_.count_hit_[idx_ctns] ? mp_.prob_hit_log_ : mp_.prob_miss_log_;md_.count_hit_[idx_ctns] = md_.count_hit_and_miss_[idx_ctns] = 0;//判定占据if (log_odds_update >= 0 && md_.occupancy_buffer_[idx_ctns] >= mp_.clamp_max_log_){continue;}//判定空闲else if (log_odds_update <= 0 && md_.occupancy_buffer_[idx_ctns] <= mp_.clamp_min_log_){md_.occupancy_buffer_[idx_ctns] = mp_.clamp_min_log_;continue;}bool in_local = idx(0) >= min_id(0) && idx(0) <= max_id(0) && idx(1) >= min_id(1) &&idx(1) <= max_id(1) && idx(2) >= min_id(2) && idx(2) <= max_id(2);if (!in_local){//不在局部更新范围,将体素设定为空闲md_.occupancy_buffer_[idx_ctns] = mp_.clamp_min_log_;}//在局部更新范围,求得正确的占据概率md_.occupancy_buffer_[idx_ctns] =std::min(std::max(md_.occupancy_buffer_[idx_ctns] + log_odds_update, mp_.clamp_min_log_),mp_.clamp_max_log_);}
}

inline bool GridMap::isInMap(const Eigen::Vector3d& pos) {//map_max_boundary,map_min_boundary表示地图的位置（pos）范围/*mp_.map_min_boundary_ = mp_.map_origin_;mp_.map_max_boundary_ = mp_.map_origin_ + mp_.map_size_;*/if (pos(0) < mp_.map_min_boundary_(0) + 1e-4 || pos(1) < mp_.map_min_boundary_(1) + 1e-4 ||pos(2) < mp_.map_min_boundary_(2) + 1e-4) {// cout << "less than min range!" << endl;return false;}if (pos(0) > mp_.map_max_boundary_(0) - 1e-4 || pos(1) > mp_.map_max_boundary_(1) - 1e-4 ||pos(2) > mp_.map_max_boundary_(2) - 1e-4) {return false;}return true;
}

Eigen::Vector3d GridMap::closetPointInMap(const Eigen::Vector3d &pt, const Eigen::Vector3d &camera_pt)
{Eigen::Vector3d diff = pt - camera_pt;Eigen::Vector3d max_tc = mp_.map_max_boundary_ - camera_pt;Eigen::Vector3d min_tc = mp_.map_min_boundary_ - camera_pt;double min_t = 1000000;for (int i = 0; i < 3; ++i){if (fabs(diff[i]) > 0){double t1 = max_tc[i] / diff[i];if (t1 > 0 && t1 < min_t)min_t = t1;double t2 = min_tc[i] / diff[i];if (t2 > 0 && t2 < min_t)min_t = t2;}}return camera_pt + (min_t - 1e-3) * diff;
}

int GridMap::setCacheOccupancy(Eigen::Vector3d pos, int occ)
{if (occ != 1 && occ != 0)return INVALID_IDX;Eigen::Vector3i id;posToIndex(pos, id);int idx_ctns = toAddress(id);//体素占据和空闲,md_.count_hit_and_miss_均会自增1md_.count_hit_and_miss_[idx_ctns] += 1;
//md_.count_hit_and_miss_[idx_ctns] == 1表示这个体素首次被发现,idx_ctns表示这个体素的一维索引if (md_.count_hit_and_miss_[idx_ctns] == 1){// md_.cache_voxel_存储体素的三维索引md_.cache_voxel_.push(id);}if (occ == 1)md_.count_hit_[idx_ctns] += 1;return idx_ctns;
}

3. void GridMap::clearAndInflateLocalMap()

void GridMap::clearAndInflateLocalMap()
{/*clear outside local*/const int vec_margin = 5;//栅格级别//local_bound_min_表示更新栅格的范围,local_map_margin_ = 10 表示局部栅格地图的更新和清除Eigen::Vector3i min_cut = md_.local_bound_min_ -Eigen::Vector3i(mp_.local_map_margin_, mp_.local_map_margin_, mp_.local_map_margin_);Eigen::Vector3i max_cut = md_.local_bound_max_ +Eigen::Vector3i(mp_.local_map_margin_, mp_.local_map_margin_, mp_.local_map_margin_);boundIndex(min_cut);boundIndex(max_cut);Eigen::Vector3i min_cut_m = min_cut - Eigen::Vector3i(vec_margin, vec_margin, vec_margin);Eigen::Vector3i max_cut_m = max_cut + Eigen::Vector3i(vec_margin, vec_margin, vec_margin);boundIndex(min_cut_m);boundIndex(max_cut_m);// clear data outside the local range(设为空闲)for (int x = min_cut_m(0); x <= max_cut_m(0); ++x)for (int y = min_cut_m(1); y <= max_cut_m(1); ++y){for (int z = min_cut_m(2); z < min_cut(2); ++z){int idx = toAddress(x, y, z);// md_.occupancy_buffer_[idx] =-1.99243-0.01=-2.00243 < mp_.clamp_min_log_=-1.99243.相当于是设为空闲md_.occupancy_buffer_[idx] = mp_.clamp_min_log_ - mp_.unknown_flag_;}for (int z = max_cut(2) + 1; z <= max_cut_m(2); ++z){int idx = toAddress(x, y, z);md_.occupancy_buffer_[idx] = mp_.clamp_min_log_ - mp_.unknown_flag_;}}for (int z = min_cut_m(2); z <= max_cut_m(2); ++z)for (int x = min_cut_m(0); x <= max_cut_m(0); ++x){for (int y = min_cut_m(1); y < min_cut(1); ++y){int idx = toAddress(x, y, z);md_.occupancy_buffer_[idx] = mp_.clamp_min_log_ - mp_.unknown_flag_;}for (int y = max_cut(1) + 1; y <= max_cut_m(1); ++y){int idx = toAddress(x, y, z);md_.occupancy_buffer_[idx] = mp_.clamp_min_log_ - mp_.unknown_flag_;}}for (int y = min_cut_m(1); y <= max_cut_m(1); ++y)for (int z = min_cut_m(2); z <= max_cut_m(2); ++z){for (int x = min_cut_m(0); x < min_cut(0); ++x){int idx = toAddress(x, y, z);md_.occupancy_buffer_[idx] = mp_.clamp_min_log_ - mp_.unknown_flag_;}for (int x = max_cut(0) + 1; x <= max_cut_m(0); ++x){int idx = toAddress(x, y, z);md_.occupancy_buffer_[idx] = mp_.clamp_min_log_ - mp_.unknown_flag_;}}// inflate occupied voxels to compensate robot size//obstacles_inflation_=0.099,resolution_=0.1,ceil向上取整，inf_step=1.0int inf_step = ceil(mp_.obstacles_inflation_ / mp_.resolution_);// int inf_step_z = 1;vector<Eigen::Vector3i> inf_pts(pow(2 * inf_step + 1, 3));//inf_pts是27行1列的容器Eigen::Vector3i inf_pt;// clear outdated datafor (int x = md_.local_bound_min_(0); x <= md_.local_bound_max_(0); ++x)for (int y = md_.local_bound_min_(1); y <= md_.local_bound_max_(1); ++y)for (int z = md_.local_bound_min_(2); z <= md_.local_bound_max_(2); ++z){md_.occupancy_buffer_inflate_[toAddress(x, y, z)] = 0;}// inflate obstaclesfor (int x = md_.local_bound_min_(0); x <= md_.local_bound_max_(0); ++x)for (int y = md_.local_bound_min_(1); y <= md_.local_bound_max_(1); ++y)for (int z = md_.local_bound_min_(2); z <= md_.local_bound_max_(2); ++z){//thresh log = mp_.min_occupancy_log_ = 1.38629/*  hit: 0.619039miss: -0.619039min log: -1.99243max: 2.19722thresh log: 1.38629  *///判定为占据if (md_.occupancy_buffer_[toAddress(x, y, z)] > mp_.min_occupancy_log_){//Eigen::Vector3i(x, y, z)栅格级别,左右各膨胀1*0.1=0.1m,膨胀后的栅格点的坐标存在inf_pts中inflatePoint(Eigen::Vector3i(x, y, z), inf_step, inf_pts);for (int k = 0; k < (int)inf_pts.size(); ++k){inf_pt = inf_pts[k];int idx_inf = toAddress(inf_pt);if (idx_inf < 0 ||idx_inf >= mp_.map_voxel_num_(0) * mp_.map_voxel_num_(1) * mp_.map_voxel_num_(2)){continue;}md_.occupancy_buffer_inflate_[idx_inf] = 1;}}}// add virtual ceiling to limit flight height//mp_.virtual_ceil_height_默认值=3.0, mp_.map_origin_(2) = mp_.ground_height_=-0.01if (mp_.virtual_ceil_height_ > -0.5) {int ceil_id = floor((mp_.virtual_ceil_height_ - mp_.map_origin_(2)) * mp_.resolution_inv_);// ceil_id = 30 是栅格级别,相当于30*0.1=3mfor (int x = md_.local_bound_min_(0); x <= md_.local_bound_max_(0); ++x)for (int y = md_.local_bound_min_(1); y <= md_.local_bound_max_(1); ++y) {md_.occupancy_buffer_inflate_[toAddress(x, y, ceil_id)] = 1;}}
}

inline void GridMap::inflatePoint(const Eigen::Vector3i& pt, int step, vector<Eigen::Vector3i>& pts) {int num = 0;/* ---------- all inflate ---------- */for (int x = -step; x <= step; ++x)for (int y = -step; y <= step; ++y)for (int z = -step; z <= step; ++z) {pts[num++] = Eigen::Vector3i(pt(0) + x, pt(1) + y, pt(2) + z);}
}

1.图中红色阴影部分的体素被设为空闲，对应代码中的 //clear data outside the local range.
2.黑色框内的占据体素会被膨胀,左右各膨胀1*0.1=0.1m,而黑色框与蓝色框的边边距离为local_map_margin_*0.1=1m.

  //local_bound_min_表示更新栅格的范围,local_map_margin_ = 10 表示局部栅格地图的更新和清除Eigen::Vector3i min_cut = md_.local_bound_min_ -Eigen::Vector3i(mp_.local_map_margin_, mp_.local_map_margin_, mp_.local_map_margin_);Eigen::Vector3i max_cut = md_.local_bound_max_ +Eigen::Vector3i(mp_.local_map_margin_, mp_.local_map_margin_, mp_.local_map_margin_);

4. vis_timer_ = node_.createTimer(ros::Duration(0.05), &GridMap::visCallback, this);

void GridMap::visCallback(const ros::TimerEvent & /*event*/)
{publishMapInflate(true);publishMap();
}

void GridMap::publishMapInflate(bool all_info)
{if (map_inf_pub_.getNumSubscribers() <= 0)return;pcl::PointXYZ pt;pcl::PointCloud<pcl::PointXYZ> cloud;Eigen::Vector3i min_cut = md_.local_bound_min_;Eigen::Vector3i max_cut = md_.local_bound_max_;if (all_info){int lmm = mp_.local_map_margin_;//lmm=10,栅格级别min_cut -= Eigen::Vector3i(lmm, lmm, lmm);max_cut += Eigen::Vector3i(lmm, lmm, lmm);}boundIndex(min_cut);boundIndex(max_cut);for (int x = min_cut(0); x <= max_cut(0); ++x)for (int y = min_cut(1); y <= max_cut(1); ++y)for (int z = min_cut(2); z <= max_cut(2); ++z){if (md_.occupancy_buffer_inflate_[toAddress(x, y, z)] == 0)continue;Eigen::Vector3d pos;indexToPos(Eigen::Vector3i(x, y, z), pos);if (pos(2) > mp_.visualization_truncate_height_)//visualization_truncate_height_ = 2.9,可视化的最大高度的障碍continue;pt.x = pos(0);pt.y = pos(1);pt.z = pos(2);cloud.push_back(pt);}cloud.width = cloud.points.size();cloud.height = 1;cloud.is_dense = true;cloud.header.frame_id = mp_.frame_id_;sensor_msgs::PointCloud2 cloud_msg;pcl::toROSMsg(cloud, cloud_msg);map_inf_pub_.publish(cloud_msg);// ROS_INFO("pub map");
}

void GridMap::publishMap()
{if (map_pub_.getNumSubscribers() <= 0)return;pcl::PointXYZ pt;pcl::PointCloud<pcl::PointXYZ> cloud;Eigen::Vector3i min_cut = md_.local_bound_min_;Eigen::Vector3i max_cut = md_.local_bound_max_;int lmm = mp_.local_map_margin_ / 2;min_cut -= Eigen::Vector3i(lmm, lmm, lmm);max_cut += Eigen::Vector3i(lmm, lmm, lmm);boundIndex(min_cut);boundIndex(max_cut);for (int x = min_cut(0); x <= max_cut(0); ++x)for (int y = min_cut(1); y <= max_cut(1); ++y)for (int z = min_cut(2); z <= max_cut(2); ++z){//判定为空闲if (md_.occupancy_buffer_[toAddress(x, y, z)] < mp_.min_occupancy_log_)continue;Eigen::Vector3d pos;indexToPos(Eigen::Vector3i(x, y, z), pos);if (pos(2) > mp_.visualization_truncate_height_)continue;pt.x = pos(0);pt.y = pos(1);pt.z = pos(2);cloud.push_back(pt);}cloud.width = cloud.points.size();cloud.height = 1;cloud.is_dense = true;cloud.header.frame_id = mp_.frame_id_;sensor_msgs::PointCloud2 cloud_msg;pcl::toROSMsg(cloud, cloud_msg);map_pub_.publish(cloud_msg);
}