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Opportunistic sampling-based active visual SLAM for underwater inspection

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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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Acknowledgments

This work was supported by the Office of Naval Research under award N00014-12-1-0092, monitored by Dr. T. Swean and T. Kick. We would like to thank J. Vaganay from Bluefin Robotics and P. Ozog for their support during testing. S. Chaves was supported by The SMART Scholarship for Service Program by the Department of Defense.

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Authors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Stephen M. Chaves & Ryan M. Eustice

  2. Korea Advanced Institute of Science and Technology, Daejeon, Korea

    Ayoung Kim

  3. ETH Zurich, Zurich, Switzerland

    Enric Galceran

Authors
  1. Stephen M. Chaves
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  2. Ayoung Kim
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  3. Enric Galceran
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  4. Ryan M. Eustice
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Corresponding author

Correspondence to Stephen M. Chaves.

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Chaves, S.M., Kim, A., Galceran, E. et al. Opportunistic sampling-based active visual SLAM for underwater inspection. Auton Robot 40, 1245–1265 (2016). https://doi.org/10.1007/s10514-016-9597-6

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  • DOI: https://doi.org/10.1007/s10514-016-9597-6

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