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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#

  1. LiDAR data segmented.
  2. 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#

  1. Launch a ground filter algorithm from the Points Preprocessor section in the Sensing tab. (adjust the parameters to your vehicle setup).
  2. Launch a Lidar Detector from the Computing tab.
  3. 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]