Autonomous Underwater Environment Perceiving and Modeling: An Experimental Campaign With FeelHippo AUV for Forward Looking Sonar-Based Automatic Target Recognition and Data Association

Seabed inspection is one of the most sought-after applications for Autonomous Underwater Vehicles (AUVs). In particular, acoustic sensors, as Side Scan Sonars (SSSs) and Forward-Looking Sonars (FLSs), are commonly favored over optical cameras to carry out such a task being not influenced by the illumination conditions and providing high-range data. However, acoustic images are often hard to interpret with conventional automatic techniques, forcing human operators to analyze thousands of collected images to identify the so-called Objects of Potential Interest (OPIs). In this perspective, this paper reports the development of an Automatic Target Recognition (ATR) methodology to identify and localize OPIs in FLS imagery; such detections have been then exploited to realize a world model with the Probabilistic Multiple Hypothesis Anchoring (PMHA) data association algorithm. Both the ATR and world modeling systems were on-field tested at the Naval Support and Experimentation Centre (Centro di Supporto e Sperimentazione Navale - CSSN) basin, in La Spezia, Italy, in a multi-vehicle architecture by employing an acoustic channel between FeelHippo AUV and an autonomous moving buoy.