Opportunistic sampling-based active visual SLAM for underwater inspection

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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
Publisher
SPRINGER
Issue Date
2016-10
Language
English
Article Type
Article
Citation

AUTONOMOUS ROBOTS, v.40, no.7, pp.1245 - 1265

ISSN
0929-5593
DOI
10.1007/s10514-016-9597-6
URI
http://hdl.handle.net/10203/214267
Appears in Collection
CE-Journal Papers(저널논문)
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