用声学方向、俯仰和深度差实现水下多智能体协同定位
Factor-Graph-Based Passive Acoustic Navigation for Decentralized Cooperative Localization Using Bearing Elevation Depth Difference
- 基于因子图建模，融合方位角、俯仰角和深度差信息
- 在模拟环境中定位精度优于传统航位推算方法
- 适合水下机器人团队在通信受限时使用
由于水下通信受限，精确且可扩展的水下多智能体定位仍是关键挑战。本文提出一种基于因子图表示的多智能体定位框架，融合方位角、俯仰角和深度差（BEDD）信息。利用来自声学信号的反向超短基线（inverted-USBL）测得的方位角与俯仰角以及相对深度数据，实现自主水下航行器（AUVs）团队的协作定位。我们在HoloOcean水下仿真环境中对多艘AUV进行了验证，结果表明该方法相比航位推算显著提升了定位精度。同时，研究了方位角与俯仰角测量异常值的影响，强调了对声学信号需采用鲁棒的异常值剔除技术。
原文摘要 · Abstract (English)
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.
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