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

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Opportunistic sampling-based active SLAM for underwater visual inspection

Summary


Stephen M. Chaves, Ayoung Kim, Enric Galceran and Ryan M. Eustice, Opportunistic sampling-based active SLAM for underwater visual inspection. Autonomous Robots, 40(7):1245-1265, 2016.

Abstract

This paper reports on an active SLAM framework for performing large-scale inspections with an underwater robot. We propose a path planning algorithm integrated with visual SLAM that plans loop-closure paths in order to decrease navigation uncertainty. While loop-closing revisit actions bound the robot's uncertainty, they also lead to redundant area coverage and increased path length. Our proposed opportunistic framework leverages sampling-based techniques and information filtering to plan revisit paths that are coverage efficient. We employ Gaussian process regression for modeling the prediction of camera registrations and use a two-step optimization procedure for selecting revisit actions. We show that the proposed method offers many benefits over existing solutions and good performance for bounding navigation uncertainty in long-term autonomous operations with hybrid simulation experiments and real-world field trials performed by an underwater inspection robot.

Bibtex entry

@ARTICLE { schaves-2016a,
    AUTHOR = { Stephen M. Chaves and Ayoung Kim and Enric Galceran and Ryan M. Eustice },
    TITLE = { Opportunistic sampling-based active {SLAM} for underwater visual inspection },
    JOURNAL = { Autonomous Robots },
    YEAR = { 2016 },
    VOLUME = { 40 },
    NUMBER = { 7 },
    PAGES = { 1245--1265 },
}