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 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 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.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Pierre Sermanet, Raia Hadsell, Marco Scoffier, Urs Muller, Yann LeCun
• Keywords: Category Uncertainties, IROS 2008, LAGR Off-road Robot, Long-range Visual Detection, Robotics