Potential Negative Obstacle Detection by Occlusion Labeling
Conference Paper, Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2168 - 2173, October, 2007
Abstract
In this paper, we present an approach for potential negative obstacle detection based on missing data interpretation that extends traditional techniques driven by data only which capture the occupancy of the scene. The approach is decomposed into three steps: three-dimensional (3-D) data accumulation and low level classification, 3-D occluder propagation, and context-based occlusion labeling. The approach is validated using logged laser data collected in various outdoor natural terrains and also demonstrated live on-board the Demo-III eXperimental Unmanned Vehicle (XUV).
BibTeX
@conference{Heckman-2007-9852,author = {Nicholas Heckman and Jean-Francois Lalonde and Nicolas Vandapel and Martial Hebert},
title = {Potential Negative Obstacle Detection by Occlusion Labeling},
booktitle = {Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems},
year = {2007},
month = {October},
pages = {2168 - 2173},
keywords = {negative obstacle, mobility, terrain classification, autonomous robot},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.