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Erschienen in: Autonomous Robots 7/2016

01.10.2016

Opportunistic sampling-based active visual SLAM for underwater inspection

verfasst von: Stephen M. Chaves, Ayoung Kim, Enric Galceran, Ryan M. Eustice

Erschienen in: Autonomous Robots | Ausgabe 7/2016

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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.

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Metadaten
Titel
Opportunistic sampling-based active visual SLAM for underwater inspection
verfasst von
Stephen M. Chaves
Ayoung Kim
Enric Galceran
Ryan M. Eustice
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
Autonomous Robots / Ausgabe 7/2016
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-016-9597-6

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