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

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.