Research on the Machine Vision Positioning Method Applied in the Docking System

Lin, Ri; Lin, Mingwei; Li, Dejun; Zhong, Lijia; Zhi, Hui

doi:10.1109/ieeeconf38699.2020.9389406

Cited by 2 publications

(1 citation statement)

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“…Zhejiang University [37] employed a strategy of switching between single and stereo cameras based on the number of observed lights. When three lights were detected, a monocular localization method was used, while fewer observed lights prompted a switch to stereo localization.…”

Section: Underwater Visual Navigation and Docking Based On Binocular ...mentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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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.

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Section: Underwater Visual Navigation and Docking Based On Binocular ...mentioning

confidence: 99%