Mapping And Planning Under Uncertainty In Mobile Robots With Long-Range Perception

Recent advances in self-supervised learning have enabled very long-range visual detection of obstacles and pathways (to 100 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 the 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.