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. Note to Practitioners—Autonomous robots can benefit underwater monitoring of deep areas. Our work presents a direct application of such robots, which use acoustic signals to localize underwater elements, like monitoring stations in the seafloor equipped with acoustic modems, and interact with them. The navigation is computed using a graph-based approach and can use either range or bearing information for the acoustic localization. The experiments in real scenarios are executed using preplanned trajectories, and the results prove that this system is able to localize acoustic targets within low uncertainty. The next step would be the implementation of path-planning strategies to optimize the localization process.