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autoware_shape_estimation#

Purpose#

This node calculates a refined object shape (bounding box, cylinder, convex hull) in which a pointcloud cluster fits according to a label.

Inner-workings / Algorithms#

Fitting algorithms#

  • bounding box

    L-shape fitting. See reference below for details.

  • cylinder

    cv::minEnclosingCircle

  • convex hull

    cv::convexHull

Inputs / Outputs#

Input#

Name Type Description
input tier4_perception_msgs::msg::DetectedObjectsWithFeature detected objects with labeled cluster

Output#

Name Type Description
output/objects autoware_perception_msgs::msg::DetectedObjects detected objects with refined shape

Parameters#

Name Type Description Default Range
use_corrector boolean The flag to apply rule-based corrector. true N/A
use_filter boolean The flag to apply rule-based filter true N/A
use_vehicle_reference_yaw boolean The flag to use vehicle reference yaw for corrector false N/A
use_vehicle_reference_shape_size boolean The flag to use vehicle reference shape size false N/A
use_boost_bbox_optimizer boolean The flag to use boost bbox optimizer false N/A

Assumptions / Known limits#

TBD

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},
}