ROS naviagtion analysis: costmap_2d--ObstacleLayer

构造函数

  ObstacleLayer()
  {
    costmap_ = NULL;  // this is the unsigned char* member of parent class Costmap2D.这里指明了costmap_指针保存了Obstacle这一层的地图数据
  }

对于ObstacleLater，首先分析其需要实现的Layer层的方法：

  virtual void onInitialize();
  virtual void updateBounds(double robot_x, double robot_y, double robot_yaw, double* min_x, double* min_y,double* max_x, double* max_y);
  virtual void updateCosts(costmap_2d::Costmap2D& master_grid, int min_i, int min_j, int max_i, int max_j);

  virtual void activate();
  virtual void deactivate();
  virtual void reset();

函数 onInitialize();：
首先获取参数设定的值，然后新建observation buffer：

    // create an observation buffer
observation_buffers_.push_back(boost::shared_ptr < ObservationBuffer>
 (new ObservationBuffer(topic, observation_keep_time, expected_update_rate, min_obstacle_height,max_obstacle_height, obstacle_range, raytrace_range, *tf_, global_frame_,sensor_frame, transform_tolerance)));

    // check if we'll add this buffer to our marking observation buffers
    if (marking)
      marking_buffers_.push_back(observation_buffers_.back());

    // check if we'll also add this buffer to our clearing observation buffers
    if (clearing)
      clearing_buffers_.push_back(observation_buffers_.back());

然后分别对不同的sensor类型如LaserScan PointCloud PointCloud2，注册不同的回调函数。这里选LaserScan 分析其回调函数：

void ObstacleLayer::laserScanCallback(const sensor_msgs::LaserScanConstPtr& message,
                                      const boost::shared_ptr<ObservationBuffer>& buffer)
{
  // project the laser into a point cloud
  sensor_msgs::PointCloud2 cloud;
  cloud.header = message->header;

  // project the scan into a point cloud
  try
  {
    projector_.transformLaserScanToPointCloud(message->header.frame_id, *message, cloud, *tf_);
  }
  catch (tf::TransformException &ex)
  {
    ROS_WARN("High fidelity enabled, but TF returned a transform exception to frame %s: %s", global_frame_.c_str(),
             ex.what());
    projector_.projectLaser(*message, cloud);
  }

  // buffer the point cloud
  buffer->lock();
  buffer->bufferCloud(cloud);
  buffer->unlock();
}

其中buffer->bufferCloud(cloud); 实际上是sensor_msgs::PointCloud2 >>>pcl::PCLPointCloud2 >>> pcl::PointCloud < pcl::PointXYZ > ; 然后才调用void ObservationBuffer::bufferCloud(const pcl::PointCloud<pcl::PointXYZ>& cloud) ：

void ObservationBuffer::bufferCloud(const pcl::PointCloud<pcl::PointXYZ>& cloud)
{
  Stamped < tf::Vector3 > global_origin;

  // create a new observation on the list to be populated
  observation_list_.push_front(Observation());

  // check whether the origin frame has been set explicitly or whether we should get it from the cloud
  string origin_frame = sensor_frame_ == "" ? cloud.header.frame_id : sensor_frame_;

  try
  {
    // given these observations come from sensors... we'll need to store the origin pt of the sensor
    Stamped < tf::Vector3 > local_origin(tf::Vector3(0, 0, 0),
                            pcl_conversions::fromPCL(cloud.header).stamp, origin_frame);
    tf_.waitForTransform(global_frame_, local_origin.frame_id_, local_origin.stamp_, ros::Duration(0.5));
    tf_.transformPoint(global_frame_, local_origin, global_origin);
    observation_list_.front().origin_.x = global_origin.getX();
    observation_list_.front().origin_.y = global_origin.getY();
    observation_list_.front().origin_.z = global_origin.getZ();

    // make sure to pass on the raytrace/obstacle range of the observation buffer to the observations
    observation_list_.front().raytrace_range_ = raytrace_range_;
    observation_list_.front().obstacle_range_ = obstacle_range_;

    pcl::PointCloud < pcl::PointXYZ > global_frame_cloud;

    // transform the point cloud
    pcl_ros::transformPointCloud(global_frame_, cloud, global_frame_cloud, tf_);
    global_frame_cloud.header.stamp = cloud.header.stamp;
//上面的操作都是针对 observation_list_.front()的一些meta数据作赋值
下面的操作是对(observation_list_.front().cloud_)作赋值操作，
    // now we need to remove observations from the cloud that are below or above our height thresholds
    pcl::PointCloud < pcl::PointXYZ > &observation_cloud = *(observation_list_.front().cloud_);
    unsigned int cloud_size = global_frame_cloud.points.size();
    observation_cloud.points.resize(cloud_size);
    unsigned int point_count = 0;

    // copy over the points that are within our height bounds
    for (unsigned int i = 0; i < cloud_size; ++i)
    {
      if (global_frame_cloud.points[i].z <= max_obstacle_height_
          && global_frame_cloud.points[i].z >= min_obstacle_height_)
      {
        observation_cloud.points[point_count++] = global_frame_cloud.points[i];
      }
    }

    // resize the cloud for the number of legal points
    observation_cloud.points.resize(point_count);
    observation_cloud.header.stamp = cloud.header.stamp;
    observation_cloud.header.frame_id = global_frame_cloud.header.frame_id;
  }
  catch (TransformException& ex)
  {
    // if an exception occurs, we need to remove the empty observation from the list
    observation_list_.pop_front();
    ROS_ERROR("TF Exception that should never happen for sensor frame: %s, cloud frame: %s, %s", sensor_frame_.c_str(),
              cloud.header.frame_id.c_str(), ex.what());
    return;
  }

  // if the update was successful, we want to update the last updated time
  last_updated_ = ros::Time::now();

  // we'll also remove any stale observations from the list
  //这个操作会将timestamp较早的点都移除出observation_list_
  purgeStaleObservations();
}

以下重点分析updateBounds：

void ObstacleLayer::updateBounds(double robot_x, double robot_y, double robot_yaw, double* min_x,double* min_y, double* max_x, double* max_y)
{
  if (rolling_window_)
    updateOrigin(robot_x - getSizeInMetersX() / 2, robot_y - getSizeInMetersY() / 2);
  if (!enabled_)
    return;
  useExtraBounds(min_x, min_y, max_x, max_y);

  bool current = true;
  std::vector<Observation> observations, clearing_observations;

  // get the marking observations 
 current = current && getMarkingObservations(observations);
  // get the clearing observations
 current = current &&getClearingObservations(clearing_observations);

  // update the global current status
  current_ = current;

  // raytrace freespace
  for (unsigned int i = 0; i < clearing_observations.size(); ++i)
  {
    raytraceFreespace(clearing_observations[i], min_x, min_y, max_x, max_y);//首先清理出传感器到被测物之间的区域，标记为FREE_SPACE
  }

  // place the new obstacles into a priority queue... each with a priority of zero to begin with
  for (std::vector<Observation>::const_iterator it = observations.begin(); it != observations.end(); ++it)
  {
    const Observation& obs = *it;
    const pcl::PointCloud<pcl::PointXYZ>& cloud = *(obs.cloud_);
    double sq_obstacle_range = obs.obstacle_range_ * obs.obstacle_range_;
    for (unsigned int i = 0; i < cloud.points.size(); ++i)
    {
      double px = cloud.points[i].x, py = cloud.points[i].y, pz = cloud.points[i].z;
      // if the obstacle is too high or too far away from the robot we won't add it
      if (pz > max_obstacle_height_)
      {
        ROS_DEBUG("The point is too high");
        continue;
      }
      // compute the squared distance from the hitpoint to the pointcloud's origin
double sq_dist = 
(px - obs.origin_.x) * (px - obs.origin_.x) + (py - obs.origin_.y) * (py - obs.origin_.y)
 + (pz - obs.origin_.z) * (pz - obs.origin_.z);

      // if the point is far enough away... we won't consider it
      if (sq_dist >= sq_obstacle_range)
      {
        ROS_DEBUG("The point is too far away");
        continue;
      }
      // now we need to compute the map coordinates for the observation
      unsigned int mx, my;
      if (!worldToMap(px, py, mx, my))
      {
        ROS_DEBUG("Computing map coords failed");
        continue;
      }
      unsigned int index = getIndex(mx, my);
      costmap_[index] = LETHAL_OBSTACLE;
      touch(px, py, min_x, min_y, max_x, max_y);
    }
  }

  updateFootprint(robot_x, robot_y, robot_yaw, min_x, min_y, max_x, max_y);
}

函数raytraceFreespace：
会首先处理测量值越界的问题，然后调用

    MarkCell marker(costmap_, FREE_SPACE);
    // and finally... we can execute our trace to clear obstacles along that line
    raytraceLine(marker, x0, y0, x1, y1, cell_raytrace_range);
    updateRaytraceBounds(ox, oy, wx, wy, clearing_observation.raytrace_range_, min_x, min_y, max_x, max_y);

最终raytraceLine(marker, x0, y0, x1, y1, cell_raytrace_range); 会将所有在(x0,y0)>>(x1,y1)之间的所有cell标记为FREE_SPACE。而updateRaytraceBounds 会根据测量的距离，更新扩张（min_x, min_y, max_x, max_y）。
updateBounds 在根据测量数据完成clear 操作之后，就开始了mark 操作，对每个测量到的点，标记为obstacle ：

 double px = cloud.points[i].x, py = cloud.points[i].y, pz = cloud.points[i].z;

      // if the obstacle is too high or too far away from the robot we won't add it
      if (pz > max_obstacle_height_)
      {
        ROS_DEBUG("The point is too high");
        continue;
      }

      // compute the squared distance from the hitpoint to the pointcloud's origin
      double sq_dist = (px - obs.origin_.x) * (px - obs.origin_.x) + (py - obs.origin_.y) * (py - obs.origin_.y)
          + (pz - obs.origin_.z) * (pz - obs.origin_.z);

      // if the point is far enough away... we won't consider it
      if (sq_dist >= sq_obstacle_range)
      {
        ROS_DEBUG("The point is too far away");
        continue;
      }

      // now we need to compute the map coordinates for the observation
      unsigned int mx, my;
      if (!worldToMap(px, py, mx, my))
      {
        ROS_DEBUG("Computing map coords failed");
        continue;
      }

      unsigned int index = getIndex(mx, my);
      costmap_[index] = LETHAL_OBSTACLE;
      touch(px, py, min_x, min_y, max_x, max_y);
    }

函数 updateFootprint：

void ObstacleLayer::updateFootprint(double robot_x, double robot_y, double robot_yaw, double* min_x, double* min_y,
                                    double* max_x, double* max_y)
{
    if (!footprint_clearing_enabled_) return;
    transformFootprint(robot_x, robot_y, robot_yaw, getFootprint(), transformed_footprint_);//这里获得了在当前机器人位姿（robot_x, robot_y, robot_yaw）条件下，机器人轮廓点在global坐标系下的值

    for (unsigned int i = 0; i < transformed_footprint_.size(); i++)
    {
      touch(transformed_footprint_[i].x, transformed_footprint_[i].y, min_x, min_y, max_x, max_y);//再次保留或者扩张Bounds
    }
}

函数 updateCosts：

void ObstacleLayer::updateCosts(costmap_2d::Costmap2D& master_grid, int min_i, int min_j, int max_i, int max_j)
{
  if (!enabled_)
    return;

  if (footprint_clearing_enabled_)
  {
    setConvexPolygonCost(transformed_footprint_, costmap_2d::FREE_SPACE);//设置机器人轮廓所在区域为FREE_SPACE
  }

  switch (combination_method_)
  {
    case 0:  // Overwrite调用的CostmapLayer提供的方法
      updateWithOverwrite(master_grid, min_i, min_j, max_i, max_j);
      break;
    case 1:  // Maximum
      updateWithMax(master_grid, min_i, min_j, max_i, max_j);
      break;
    default:  // Nothing
      break;
  }
}

ObstacleLayer 主要内容就是这些～～～接下来是InflationLayer