  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.
