PeRL STUDIES AUTONOMOUS NAVIGATION & MAPPING FOR MOBILE ROBOTS IN A PRIORI UNKNOWN ENVIRONMENTS.
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Opportunistic sampling-based planning for active visual SLAM
Summary
Abstract
This paper reports on an active visual SLAM path planning algorithm that plans loop-closure paths in order to decrease visual navigation uncertainty. Loop-closing revisit actions bound the robot's uncertainty but also contribute to redundant area coverage and increased path length. We propose an opportunistic path planner that leverages sampling-based techniques and information filtering for planning revisit paths that are coverage efficient. Our algorithm employs Gaussian Process regression for modeling the prediction of camera registrations and uses a two-step optimization for selecting revisit actions. We show that the proposed method outperforms existing solutions for bounding navigation uncertainty with a hybrid simulation experiment using a real-world dataset collected by a ship hull inspection robot.
Bibtex entry
@INPROCEEDINGS { schaves-2014a,
AUTHOR = { Stephen M. Chaves and Ayoung Kim and Ryan M. Eustice },
TITLE = { Opportunistic sampling-based planning for active visual {SLAM} },
BOOKTITLE = { Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems },
YEAR = { 2014 },
MONTH = { September },
ADDRESS = { Chicago, IL, USA },
PAGES = { 3073--3080 },
}