PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.

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Probabilistic obstacle partitioning of monocular video for autonomous vehicles

Summary


Ryan W. Wolcott and Ryan M. Eustice, Probabilistic obstacle partitioning of monocular video for autonomous vehicles. In Proceedings of the British Machine Vision Conference, pages 1-12, York, UK, September 2016.

Abstract

This paper reports on visual obstacle detection from a monocular camera for autonomous vehicles. By leveraging a textured prior map, we propose a probabilistic formulation for finding the optimal image partition that separates obstacles from ground-plane. Our key insight is the use of a prior map that enables ground appearance models conditioned on prior map texture and a probabilistic optical flow vector formulation derived from known scene structure and camera egomotion. We evaluate our methods on a challenging urban setting using data collected on our autonomous platform and we demonstrate that a notion of obstacles in the camera frame can improve visual localization quality.

Bibtex entry

@INPROCEEDINGS { rwolcott-2016a,
    AUTHOR = { Ryan W. Wolcott and Ryan M. Eustice },
    TITLE = { Probabilistic obstacle partitioning of monocular video for autonomous vehicles },
    BOOKTITLE = { Proceedings of the British Machine Vision Conference },
    YEAR = { 2016 },
    MONTH = { September },
    ADDRESS = { York, UK },
    PAGES = { 1--12 },
}