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Potential Negative Obstacle Detection by Occlusion Labeling
N. Heckman, N. Vandapel, and M. Hebert
IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2007.

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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).

Sponsor: Army Research Laboratory
Grant ID: DAAD19-01-209912

Associated centers: VASC and FRC
Associated lab/group: NavLab
Associated project: CTA Robotics

N. Heckman, N. Vandapel, and M. Hebert, "Potential Negative Obstacle Detection by Occlusion Labeling," IEEE/RSJ International Conference on Intelligent Robots and Systems, October, 2007.

@inproceedings{Heckman_2007_6073,
   author = "Nicholas Heckman and Nicolas Vandapel and Martial Hebert",
   title = "Potential Negative Obstacle Detection by Occlusion Labeling",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems",
   month = "October",
   year = "2007"
}