Opportunistic sampling-based planning for active visual SLAM

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.