On the Segmentation of 3D LIDAR Point Clouds

摘要：

This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e.g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e.g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.

作者: B. Douillard J. Underwood N. Kuntz V. Vlaskine A. Quadros P. Morton A. Frenkel

作者单位: The Australian Centre for Field Robotics,The University of Sydney,Australia

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 2798-2805

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）