ICRA 2011 Paper Abstract


Paper WeA110.2

Douillard, Bertrand (University of Syndey), Underwood, James Patrick (The University of Sydney), Kuntz, Noah (Drexel University), Vlaskine, Vsevolod (Australian Centre for Field Robotics, Sydney University), Quadros, Alastair James (University of Sydney), Morton, Peter (Australian Centre for Field Robotics), Frenkel, Alon (The Australian Centre for Field Robotics, The University of Sydn)

On the Segmentation of 3D LIDAR Point Clouds

Scheduled for presentation during the Regular Sessions "Mapping and Navigation I" (WeA110), Wednesday, May 11, 2011, 08:35−08:50, Room 5E

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Mapping, Field Robots, AI Reasoning Methods


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.



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