Abstract
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.
Abstract (translated)
增加的水下车辆的需求突出了在检查任务中实现稳健本地化解决方案的必要性。在这项工作中，我们提出了一个适用于环境中有稀疏分布的人类资产的水下全局定位算法，用于自主水下车辆（AUVs）。我们的方法利用了在同一多束前进式声呐（FSD）获得的声呐数据流中应用的两个协同数据解释前景。这些观察结果可以融合到一个粒子滤波器（PF）中，以便更重地考虑属于高可能性区域的分子的权重，或者解决对称模糊性。在模拟水下环境中进行初步实验，类似于真实水下植物，产生了积极的结果。这项工作代表了该方法未来发展和真实世界场景中进行详细评估的开端。
URL
https://arxiv.org/abs/2405.01971
