The increasing demand for underwater vehicles highlights the necessity for robust localization solutions in inspection missions. In this work, we present a novel real-time sonar-based underwater global positioning algorithm for AUVs (Autonomous Underwater Vehicles) designed for environments with a sparse distribution of human-made assets. Our approach exploits two synergistic data interpretation frontends applied to the same stream of sonar data acquired by a multibeam Forward-Looking Sonar (FSD). These observations are fused within a Particle Filter (PF) either to weigh more particles that belong to high-likelihood regions or to solve symmetric ambiguities. Preliminary experiments carried out on a simulated environment resembling a real underwater plant provided promising results. This work represents a starting point towards future developments of the method and consequent exhaustive evaluations also in real-world scenarios.

翻译：水下航行器需求的日益增长凸显了检测任务中鲁棒定位解决方案的必要性。本文提出了一种面向自主水下航行器（AUV）的新型实时声呐水下全局定位算法，专门设计用于人造设施稀疏分布的环境。该方法采用两种协同数据解释前端，应用于多波束前视声呐（FSD）获取的同一声呐数据流。这些观测值通过粒子滤波器（PF）进行融合，既可对高似然区域内的粒子赋予更高权重，也可解决对称性模糊问题。在模拟真实水下设施的仿真环境中开展的初步实验取得了令人鼓舞的结果。本研究为未来方法的发展及后续在真实场景中的全面评估奠定了坚实基础。