Factor-Graph-Based Passive Acoustic Navigation for Decentralized Cooperative Localization Using Bearing Elevation Depth Difference

TL;DR

This paper introduces a factor-graph-based framework for decentralized underwater multi-robot localization using acoustic bearing, elevation, and depth difference measurements, validated in simulation with improved accuracy over dead reckoning.

Contribution

The work presents a novel factor-graph approach incorporating bearing, elevation, and depth difference for cooperative underwater localization, addressing communication constraints.

Findings

Improved localization accuracy over dead reckoning.

Effective use of inverted-USBL measurements for multi-robot localization.

Highlighting the importance of outlier rejection in acoustic measurements.

Abstract

Accurate and scalable underwater multi-agent localization remains a critical challenge due to the constraints of underwater communication. In this work, we propose a multi-agent localization framework using a factor-graph representation that incorporates bearing, elevation, and depth difference (BEDD). Our method leverages inverted ultra-short baseline (inverted-USBL) derived azimuth and elevation measurements from incoming acoustic signals and relative depth measurements to enable cooperative localization for a multi-robot team of autonomous underwater vehicles (AUVs). We validate our approach in the HoloOcean underwater simulator with a fleet of AUVs, demonstrating improved localization accuracy compared to dead reckoning. Additionally, we investigate the impact of azimuth and elevation measurement outliers, highlighting the need for robust outlier rejection techniques for acoustic signals.