Adaptive Sensor Fusion for Realistic and Autonomous Seabed Models

As autonomy, duration, and complexity of UUV missions increases, so too does the need for access to higher fidelity simulation and planning tools to ensure mission critical success. Advanced UUV fleets are critical for maintaining future subsea military dominance, and the availability of suitable simulation environments for technology and autonomy development is limited, in part due to the significant manual labor required to process and generate simulation models. An adaptive data processing method that uses a variety of seafloor sensor and survey data, autonomously generates continuous and environmentally accurate three-dimensional seafloor models, and intelligently inserts and applies man-made or structured obstacles, will not only provide faster and more efficient seafloor modeling but will allow for accelerated development of the autonomous subsea vehicles critical to our subsea forces and operations. The Makai team, composed of Makai Ocean Engineering (Makai) and Woods Hole Oceanographic Institute (WHOI), propose to address this problem by developing a simulation environment synthesis that uses raw data plus adaptive and machine learning software to generate a realistic and continuous three-dimensional, multi-domain seafloor model. The proposed effort will leverage Makai’s Digital Terrain Models (DTM) used in the world’s leading submarine cable planning software MakaiPlan, decades of WHOI and Makai experience processing and visualizing seafloor datasets, and team members’ expertise developing embedded, machine learning (ML) algorithms.