A Sonar-based AUV Positioning System for Underwater Environments with Low Infrastructure Density
This work addresses the need for reliable AUV positioning in low-infrastructure underwater environments, but it is incremental as it builds on existing particle filter methods with preliminary simulated results.
The authors tackled the problem of robust localization for autonomous underwater vehicles in environments with sparse infrastructure by developing a novel real-time sonar-based global positioning algorithm, which showed promising results in simulated tests resembling a real underwater plant.
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.