Scan Ground Filter#

Purpose#

The purpose of this node is that remove the ground points from the input pointcloud.

Inner-workings / Algorithms#

This algorithm works by following steps,

Divide whole pointclouds into groups by horizontal angle and sort by xy-distance.
Divide sorted pointclouds of each ray into grids
Check the xy distance to previous pointcloud, if the distance is large and previous pointcloud is "no ground" and the height level of current point greater than previous point, the current pointcloud is classified as no ground.
Check vertical angle of the point compared with previous ground grid
Check the height of the point compared with predicted ground level
If vertical angle is greater than local_slope_max and related height to predicted ground level is greater than "non ground height threshold", the point is classified as "non ground"
If the vertical angle is in range of [-local_slope_max, local_slope_max] or related height to predicted ground level is smaller than non_ground_height_threshold, the point is classified as "ground"
If the vertical angle is lower than -local_slope_max or the related height to ground level is greater than detection_range_z_max, the point will be classified as out of range

Inputs / Outputs#

This implementation inherits pointcloud_preprocessor::Filter class, please refer README.

Parameters#

Node Parameters#

This implementation inherits pointcloud_preprocessor::Filter class, please refer README.

Core Parameters#

scan_ground_parameter

Name	Type	Default Value	Description
`input_frame`	string	"base_link"	frame id of input pointcloud
`output_frame`	string	"base_link"	frame id of output pointcloud
`global_slope_max_angle_deg`	double	8.0	The global angle to classify as the ground or object [deg]. A large threshold may reduce false positive of high slope road classification but it may lead to increase false negative of non-ground classification, particularly for small objects.
`local_slope_max_angle_deg`	double	10.0	The local angle to classify as the ground or object [deg] when comparing with adjacent point. A small value enhance accuracy classification of object with inclined surface. This should be considered together with `split_points_distance_tolerance` value.
`radial_divider_angle_deg`	double	1.0	The angle which divide the whole pointcloud to sliced group [deg]
`split_points_distance_tolerance`	double	0.2	The xy-distance threshold to distinguish far and near [m]
`split_height_distance`	double	0.2	The height threshold to distinguish ground and non-ground pointcloud when comparing with adjacent points [m]. A small threshold improves classification of non-ground point, especially for high elevation resolution pointcloud lidar. However, it might cause false positive for small step-like road surface or misaligned multiple lidar configuration.
`use_virtual_ground_point`	bool	true	whether to use the ground center of front wheels as the virtual ground point.
`detection_range_z_max`	float	2.5	Maximum height of detection range [m], applied only for elevation_grid_mode
`center_pcl_shift`	float	0.0	The x-axis offset of addition LiDARs from vehicle center of mass [m], recommended to use only for additional LiDARs in elevation_grid_mode
`non_ground_height_threshold`	float	0.2	Height threshold of non ground objects [m] as `split_height_distance` and applied only for elevation_grid_mode
`grid_mode_switch_radius`	float	20.0	The distance where grid division mode change from by distance to by vertical angle [m], applied only for elevation_grid_mode
`grid_size_m`	float	0.5	The first grid size [m], applied only for elevation_grid_mode. A large value enhances the prediction stability for ground surface. suitable for rough surface or multiple lidar configuration.
`gnd_grid_buffer_size`	uint16	4	Number of grids using to estimate local ground slope, applied only for elevation_grid_mode
`low_priority_region_x`	float	-20.0	The non-zero x threshold in back side from which small objects detection is low priority [m]
`elevation_grid_mode`	bool	true	Elevation grid scan mode option
`use_recheck_ground_cluster`	bool	true	Enable recheck ground cluster
`use_lowest_point`	bool	true	to select lowest point for reference in recheck ground cluster, otherwise select middle point

Assumptions / Known limits#

The input_frame is set as parameter but it must be fixed as base_link for the current algorithm.

(Optional) Error detection and handling#

(Optional) Performance characterization#

(Optional) References/External links#

The elevation grid idea is referred from "Shen Z, Liang H, Lin L, Wang Z, Huang W, Yu J. Fast Ground Segmentation for 3D LiDAR Point Cloud Based on Jump-Convolution-Process. Remote Sensing. 2021; 13(16):3239. https://doi.org/10.3390/rs13163239"

(Optional) Future extensions / Unimplemented parts#

Horizontal check for classification is not implemented yet.
Output ground visibility for diagnostic is not implemented yet.