Publications

 


  Douillard, B., Underwood, J.P., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P. & Frenkel, A.
On the Segmentation of 3D LIDAR Point Clouds
Proceedings of the 2011 IEEE International Conference on Robotics and Automation , pp. 2798-2805, May, 2011
Presented at 2011 IEEE International Conference on Robotics and Automation, 09 May. - 13 May. 2011

Abstract
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.

Download    PDF full text (1.68M)

Copyright Notice & Disclaimer