Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

TL;DR

This paper introduces a novel underwater exploration framework using SLAM with imaging sonar, enabling autonomous robots to efficiently map cluttered environments with high coverage and low uncertainty.

Contribution

The paper presents a new exploration system that integrates virtual maps, path planning, and uncertainty estimation for underwater robots in cluttered environments.

Findings

High coverage rate achieved during exploration

Low mapping and localization errors maintained

Framework validated in real-world harbor environment

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.