Estimation of Positions and Poses of Autonomous Underwater Vehicle Relative to Docking Station Based on Adaptive Extraction of Visual Guidance Features

Abstract

The underwater docking of autonomous underwater vehicles (AUVs) is conducive to energy supply and data exchange. A vision-based high-precision estimation of “the positions and poses of an AUV relative to a docking station” (PPARD) is a necessary condition for successful docking. Classical binarization methods have a low success rate in extracting guidance features from fuzzy underwater images, resulting in an insufficient stability of the PPARD estimation. Based on the fact that guidance lamps are blue strong point light sources, this study proposes an adaptive calculation method of binary threshold for the guidance image. To decrease the failure of guidance feature extraction, a guidance image enhancement method is proposed to strengthen the characteristic that the guidance lamps are strong point light sources with a certain area. The PPARD is estimated through solving the minimum value of the imaging error function for the vision-based extracted guidance features. The experimental results showed that the absolute estimation error for each degree of freedom in the PPARD was at most 10%, which was lower than that of the orthogonal iteration (OI) method. In addition, the proposed guidance feature extraction method proved to be better than the classical methods, with the extraction success rate reaching 87.99%.

Review of underwater visual navigation and docking: advances and challenges

“…Experimental results demonstrate that the GQPSO algorithm achieves higher matching precision and speed, especially when the number of iterations exceeds 40, achieving a matching precision of up to 100%. Lv et al [41] present an adaptive visual feature extraction method tailored to guide lights with strong blue point light sources. It enhances guide images to emphasize these features and estimates their position and orientation by minimizing imaging errors.…”