一种面向低基础设施密度水下环境的基于声呐的AUV定位系统
A Sonar-based AUV Positioning System for Underwater Environments with Low Infrastructure Density
AI总结：
本文提出一种基于多波束前视声呐与粒子滤波融合的实时水下全局定位算法，面向人造资产稀疏环境，通过双前端协同处理声呐数据以提升AUV定位鲁棒性，并在仿真水下工厂中验证了可行性。
摘要：
对水下航行器日益增长的需求凸显了在检查任务中采用鲁棒定位方案的必要性。本文提出了一种新颖的基于声呐的实时水下全局定位算法，用于自主水下航行器（AUV），并针对人造资产分布稀疏的环境设计。该方法利用两个协同的数据解释前端，处理由多波束前视声呐（FSD）获取的同一流声呐数据。这些观测结果在粒子滤波器（PF）内进行融合，既可为属于高似然区域的更多粒子赋予权重，也可解决对称歧义。在模拟真实水下工厂的仿真环境中进行的初步实验取得了有前景的结果。本研究为该方法未来的发展以及后续在真实场景中进行全面评估奠定了基础。
英文摘要：
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.
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