Shape Estimation#
L-shape fitting implementation of the paper:
@conference{Zhang-2017-26536,
author = {Xiao Zhang and Wenda Xu and Chiyu Dong and John M. Dolan},
title = {Efficient L-Shape Fitting for Vehicle Detection Using Laser Scanners},
booktitle = {2017 IEEE Intelligent Vehicles Symposium},
year = {2017},
month = {June},
keywords = {autonomous driving, laser scanner, perception, segmentation},
}
How to launch#
From RTM#
Computing tab -> Detection -> lidar_detector -> lidar_shape_estimation
Configure parameters using the [app]
button.
From the command line#
From a sourced command line:
roslaunch lidar_shape_estimation shape_estimation_clustering.launch
Launch files also include the visualization node.
Requirements#
- LiDAR data segmented.
- Objects
Parameters#
Parameter | Type | Description | Default |
---|---|---|---|
input |
String | Topic name containing the objects detected by the Lidar in 3D space. | /detection/lidar_detector/objects |
output |
String | Topic name containing the objects with the shape estimated in 3D space. | /detection/lidar_shape_estimation/objects |
Usage example#
- Launch a ground filter algorithm from the
Points Preprocessor
section in the Sensing tab. (adjust the parameters to your vehicle setup). - Launch a Lidar Detector from the Computing tab.
- Launch this node.
Node info#
Node [/lidar_shape_estimation]
Publications:
* /detection/shape_estimation/objects [autoware_perception_msgs/DetectedObjectArray]
Subscriptions:
* /detection/lidar_detector/objects [autoware_perception_msgs/DetectedObjectArray]
-------------------------
Node [/detection/shape_estimation/shape_estimation_visualization]
Publications:
* /detection/shape_estimation/objects_markers [visualization_msgs/MarkerArray]
Subscriptions:
* /detection/shape_estimation/objects [autoware_perception_msgs/DetectedObjectArray]