Probabilistic Obstacle Partitioning of Monocular Video for Autonomous Vehicles

Ryan Wolcott and Ryan Eustice

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.

Session

Posters 2

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DOI

10.5244/C.30.128
https://dx.doi.org/10.5244/C.30.128

Citation

Ryan Wolcott and Ryan Eustice. Probabilistic Obstacle Partitioning of Monocular Video for Autonomous Vehicles. In Richard C. Wilson, Edwin R. Hancock and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 128.1-128.12. BMVA Press, September 2016.

Bibtex

        @inproceedings{BMVC2016_128,
        	title={Probabilistic Obstacle Partitioning of Monocular Video for Autonomous Vehicles},
        	author={Ryan Wolcott and Ryan Eustice},
        	year={2016},
        	month={September},
        	pages={128.1-128.12},
        	articleno={128},
        	numpages={12},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.128},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.128}
        }