Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments
This work addresses the problem of efficient and reliable exploration for underwater robots in cluttered settings, representing an incremental improvement with domain-specific applications.
The paper tackles autonomous underwater robot exploration in cluttered environments by developing a novel framework using SLAM with imaging sonar, which achieves high coverage rates and low mapping and localization errors in simulations and real-world tests.
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.