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Potential Negative Obstacle Detection by Occlusion Labeling

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posted on 2007-01-01, 00:00 authored by Nicholas Heckman, Jean-Francois Lalonde, Nicolas Vandapel, Martial Hebert
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).

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2007-01-01

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