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laserscan based occupancy grid map#

laserscan_based_occupancy_grid_map_sample_image

Inner-workings / Algorithms#

The basic idea is to take a 2D laserscan and ray trace it to create a time-series processed occupancy grid map.

  1. the node take a laserscan and make an occupancy grid map with one frame. ray trace is done by Bresenham's line algorithm. Bresenham's line algorithm

  2. Optionally, obstacle point clouds and raw point clouds can be received and reflected in the occupancy grid map. The reason is that laserscan only uses the most foreground point in the polar coordinate system, so it throws away a lot of information. As a result, the occupancy grid map is almost an UNKNOWN cell. Therefore, the obstacle point cloud and the raw point cloud are used to reflect what is judged to be the ground and what is judged to be an obstacle in the occupancy grid map. The black and red dots represent raw point clouds, and the red dots represent obstacle point clouds. In other words, the black points are determined as the ground, and the red point cloud is the points determined as obstacles. The gray cells are represented as UNKNOWN cells. Bresenham's line algorithm

  3. Using the previous occupancy grid map, update the existence probability using a binary Bayesian filter (1). Also, the unobserved cells are time-decayed like the system noise of the Kalman filter (2).

\[ \hat{P_{o}} = \frac{(P_{o} *P_{z})}{(P_{o}* P_{z} + (1 - P_{o}) * \bar{P_{z}})} \tag{1} \]
\[ \hat{P_{o}} = \frac{(P_{o} + 0.5 * \frac{1}{ratio})}{(\frac{1}{ratio} + 1)} \tag{2} \]

Inputs / Outputs#

Input#

Name Type Description
~/input/laserscan sensor_msgs::LaserScan laserscan
~/input/obstacle_pointcloud sensor_msgs::PointCloud2 obstacle pointcloud
~/input/raw_pointcloud sensor_msgs::PointCloud2 The overall point cloud used to input the obstacle point cloud

Output#

Name Type Description
~/output/occupancy_grid_map nav_msgs::OccupancyGrid occupancy grid map

Parameters#

Node Parameters#

Name Type Description
map_frame string map frame
base_link_frame string base_link frame
input_obstacle_pointcloud bool whether to use the optional obstacle point cloud? If this is true, ~/input/obstacle_pointcloud topics will be received.
input_obstacle_and_raw_pointcloud bool whether to use the optional obstacle and raw point cloud? If this is true, ~/input/obstacle_pointcloud and ~/input/raw_pointcloud topics will be received.
use_height_filter bool whether to height filter for ~/input/obstacle_pointcloud and ~/input/raw_pointcloud? By default, the height is set to -1~2m.
map_length double The length of the map. -100 if it is 50~50[m]
map_resolution double The map cell resolution [m]

Assumptions / Known limits#

In several places we have modified the external code written in BSD3 license.

  • occupancy_grid_map.hpp
  • cost_value.hpp
  • occupancy_grid_map.cpp

(Optional) Error detection and handling#

(Optional) Performance characterization#

Bresenham's_line_algorithm

(Optional) Future extensions / Unimplemented parts#

  • The update probability of the binary Bayesian filter is currently hard-coded and requires a code change to be modified.
  • Since there is no special support for moving objects, the probability of existence is not increased for fast objects.