Review of underwater visual navigation and docking: advances and challenges

Abstract

The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. Underwater vision navigation systems serve as a pivotal technology, encompassing various domains such as computer vision, image processing, and machine learning. Nevertheless, they confront a series of challenges. This review aims to consolidate the most recent research advancements in underwater vision navigation systems, with a particular focus on critical aspects such as beacon design, monocular and binocular vision, and detection algorithms. At present, underwater beacons can be categorized into active and passive types, with the flexibility to choose between them contingent upon the specific task and environment. On the other hand, relative to the prevalent use of monocular vision, binocular vision systems offer the potential to furnish more accurate depth information, and the seamless integration of monocular and binocular vision systems enhances the precision and stability of navigation tasks. Concerning detection algorithms, traditional feature extraction methods and deep learning approaches each exhibit their merits and drawbacks, but deep learning methods demonstrate immense potential in complex underwater environments. By summarizing and analyzing the above research results, the application of each key technology of underwater vision navigation and docking is sorted out, and its further development direction is foreseen.