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IROS
2008
IEEE

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

10 years 3 months ago
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.
Pierre Sermanet, Raia Hadsell, Marco Scoffier, Urs
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IROS
Authors Pierre Sermanet, Raia Hadsell, Marco Scoffier, Urs Muller, Yann LeCun
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