Abstract
According to the image characters in the process of vision guided Autonomous Underwater Vehicle (AUV) docking, an image-matching algorithm based on Quanta Particle Swarm Optimization (QPSO) and grey relational analysis is proposed. The algorithm combines the speediness of QPSO and the robustness of the grey relational analysis. The grey absolute correlative degree of the image gray histogram was used as fitness function, and the image-matching algorithm based on QPSO was detailed. Using images from tank test, experimental results presented to demonstrate that the proposed algorithm can fit vision guided docking need.
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