Mapping and planning under uncertainty in mobile robots with long-range perception

P. Sermanet, P. Sermanet, R. Hadsell, M. Scoffier, M. Scoffier, U. Muller, Y. LeCun
2008 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Recent advances in self-supervised learning have enabled very long-range visual detection of obstacles and pathways (to 100 hundred meters or more). Unfortunately, the category and range of regions at such large distances come with a considerable amount of uncertainty. We present a mapping and planning system that accurately represents range and category uncertainties, and accumulates the evidence from multiple frames in a principled way. The system relies on a hyperbolic-polar map centered on
more » ... he robot with a 200m radius. Map cells are histograms that accumulate evidence obtained from a self-supervised object classifier operating on image windows. The performance of the system is demonstrated on the LAGR off-road robot platform.
doi:10.1109/iros.2008.4651203 dblp:conf/iros/SermanetHSML08 fatcat:kcytzgdijfd6zdqzhqofmkigr4