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Potential Negative Obstacle Detection by Occlusion Labeling

Nicholas Heckman, Jean-Francois Lalonde, Nicolas Vandapel and Martial Hebert
Conference Paper, Carnegie Mellon University, 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).

BibTeX Reference
title = {Potential Negative Obstacle Detection by Occlusion Labeling},
author = {Nicholas Heckman and Jean-Francois Lalonde and Nicolas Vandapel and Martial Hebert},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
keyword = {negative obstacle, mobility, terrain classification, autonomous robot},
sponsor = {Army Research Laboratory},
grantID = {DAAD19-01-209912},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2007},
address = {Pittsburgh, PA},