Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

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. In this article, we present a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping 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 exploring a harbor environment.

Automated summary

This paper discusses the problem of autonomous mobile robot exploration in unknown environments, presenting a novel exploration framework for underwater robots operating in cluttered environments. The framework takes into account coverage rates, map uncertainty, and state estimation uncertainty, and is evaluated through simulated experiments and real-world applications in an underwater environment.