Modular Acoustic Graph SLAM for Underwater Monitoring With Autonomous Underwater Vehicles

This work was developed under the need for an acoustic localization system to monitor marine protected areas (MPAs) with the help of autonomous underwater vehicles (AUVs). Although the use of acoustic signals for underwater localization has been previously studied, most of the solutions rely on filter-based optimization, which is prone to linearization problems in long-term applications. Instead, we implemented a Modular Acoustic Graph Simultaneous Localization and Mapping (SLAM) algorithm that, using a factor graph framework, tracks acoustic beacons with either ranges or bearings. In addition, we developed several novel methods, like a delayed-position update for ultra-short baseline (USBL) position factor integration process, an initialization algorithm for acoustic landmarks, and the creation of a new 3D bearing factor that combines two angles. After developing the algorithm, field experiments were carried out in different areas on the coast of Catalonia. Besides the localization, some monitoring tasks were also tested, such as visual mapping of localized landmarks or optical transmission of data with seafloor stations, which helped validate the accuracy of the acoustic localization system. The results of such experiments are presented and discussed.