Mapping and planning under uncertainty in mobile robots with long-range perception
- 1 September 2008
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 2525-2530
- https://doi.org/10.1109/iros.2008.4651203
Abstract
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 hyperbolicpolar map centered on the robot with a 200 m 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.Keywords
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