Uncertainty-Aware Acoustic Localization and Mapping for Underwater Robots

TL;DR

This paper introduces an uncertainty-aware localization and mapping framework for underwater robots that accounts for environmental disturbances affecting acoustic sensors, improving robustness in dynamic wave conditions.

Contribution

It presents a novel framework that incorporates environmental disturbance-induced uncertainty into acoustic localization and mapping for underwater robots.

Findings

Effective in dynamic wave conditions

Improves robustness of underwater acoustic localization

Validated through experiments in a wave tank environment

Abstract

For underwater vehicles, robotic applications have the added difficulty of operating in highly unstructured and dynamic environments. Environmental effects impact not only the dynamics and controls of the robot but also the perception and sensing modalities. Acoustic sensors, which inherently use mechanically vibrated signals for measuring range or velocity, are particularly prone to the effects that such dynamic environments induce. This paper presents an uncertainty-aware localization and mapping framework that accounts for induced disturbances in acoustic sensing modalities for underwater robots operating near the surface in dynamic wave conditions. For the state estimation task, the uncertainty is accounted for as the added noise caused by the environmental disturbance. The mapping method uses an adaptive kernel-based method to propagate measurement and pose uncertainty into an occupancy map. Experiments are carried out in a wave tank environment to perform qualitative and quantitative evaluations of the proposed method. More details about this project can be found at https://umfieldrobotics.github.io/PUMA.github.io.