Opportunistic sampling-based active visual SLAM for underwater inspection |
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Authors: | Stephen M Chaves Ayoung Kim Enric Galceran Ryan M Eustice |
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Affiliation: | 1.University of Michigan,Ann Arbor,USA;2.Korea Advanced Institute of Science and Technology,Daejeon,Korea;3.ETH Zurich,Zurich,Switzerland |
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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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