Having an autonomous vehicle actively map an unknown environment is a difficult task and improving this capability would be extremely beneficial. In this paper, the authors extend their research on expectation-maximization (EM) exploration and virtual mappings to include real-world applications using a robot equipped with a multibeam imaging sonar to explore cluttered underwater environments.
Abstract: We consider the problem of autonomous mobile robot exploration in an unknown environment, taking into account a robot’s coverage rate, map uncertainty, and state estimation uncertainty. This paper presents a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping (SLAM) with imaging sonar. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map, which estimates the uncertainty associated with map cells throughout a robot’s workspace. We evaluate the performance of this framework in simulated experiments, showing that our algorithm maintains a high coverage rate during exploration while also maintaining low mapping and localization error. The real-world applicability of our framework is also demonstrated on an underwater remotely operated vehicle (ROV) exploring a harbor environment.
Author: Wang, J.; Chen, F.; Huang, Y.; McConnell, J.; Shan, T.; Englot, B.
Journal: IEEE Journal of Oceanic Engineering
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