A localization approach for autonomous underwater vehicles: A ROS-Gazebo framework

TL;DR

This paper introduces a ROS-Gazebo simulation framework with a realistic underwater acoustic model for localizing autonomous underwater vehicles using surface robots and acoustic multilateration, addressing the challenge of pose estimation in harsh underwater environments.

Contribution

The paper develops a novel ROS-Gazebo framework with a realistic underwater acoustic model for AUV localization, integrating surface robots, acoustic wave timing, and genetic algorithms.

Findings

Effective simulation of underwater acoustic localization

Improved pose estimation accuracy for AUVs

Framework supports testing of localization algorithms

Abstract

Autonomous Underwater Vehicles (AUVs) have the ability to operate in harsh underwater environments without endangering human lives in the process. Nevertheless, just like their ground and aerial counterparts, AUVs need to be able to estimate their own position. Yet, unlike ground and aerial robots, estimating the pose of AUVs is very challenging, with only a few high-cost technological solutions available in the market. In this paper, we present the development of a realistic underwater acoustic model, implemented within the Robot Operating System (ROS) and the Gazebo simulator framework, for localization of AUVs using a set of water surface robots, time of flight of underwater propagated acoustic waves, and a multilateration genetic algorithm approach.