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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 ondoi:10.1109/iros.2008.4651203 dblp:conf/iros/SermanetHSML08 fatcat:kcytzgdijfd6zdqzhqofmkigr4