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

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Underwater robot visual place recognition in the presence of dramatic appearance change

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Jie Li, Ryan M. Eustice and Matthew Johnson-Roberson, Underwater robot visual place recognition in the presence of dramatic appearance change. In Proceedings of the IEEE/MTS OCEANS Conference and Exhibition, pages 1-6, Washington, D.C., USA, October 2015.

Abstract

This paper reports on an algorithm for underwater visual place recognition in the presence of dramatic appearance change. Long-term visual place recognition is challenging underwater due to biofouling, corrosion, and other effects that lead to dramatic visual appearance change, which often causes traditional point-feature-based methods to perform poorly. Building upon the authors' earlier work, this paper presents an algorithm for underwater vehicle place recognition and relocalization that enables an underwater autonomous vehicle to relocalize itself to a previously-built simultaneous localization and mapping (SLAM) graph. High-level structural features are learned using a supervised learning framework that retains features that have a high potential to persist in the underwater environment. Combined with a particle filtering framework, these features are used to provide a probabilistic representation of localization confidence. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle (HAUV) for ship hull inspection.

Bibtex entry

@INPROCEEDINGS { jli-2015b,
    AUTHOR = { Jie Li and Ryan M. Eustice and Matthew Johnson-Roberson },
    TITLE = { Underwater robot visual place recognition in the presence of dramatic appearance change },
    BOOKTITLE = { Proceedings of the IEEE/MTS OCEANS Conference and Exhibition },
    YEAR = { 2015 },
    MONTH = { October },
    ADDRESS = { Washington, D.C., USA },
    PAGES = { 1--6 },
}