AUV Navigation

Autonomous underwater vehicle (AUV) navigation research focuses on improving the accuracy and reliability of AUV positioning and mapping, particularly in challenging underwater environments lacking GPS. Current efforts concentrate on fusing data from various sensors (e.g., inertial measurement units, Doppler velocity logs, acoustic arrays, and cameras) using advanced algorithms like Kalman filters (including variations such as the Unscented Kalman Filter on Manifolds) and deep learning models (e.g., neural networks for beam estimation and bathymetric mapping). These advancements are crucial for enabling more complex AUV missions, such as precise underwater docking, autonomous cave exploration, and improved seabed mapping, ultimately enhancing scientific understanding of the ocean and supporting various underwater applications.