Abstract

To improve the accuracy and speed of existing binocular vision methods in practical environments, this article presents an optimization method for accurate and stable 3-D pose measurement based on binocular vision. First, in the stereo-image-matching stage, we introduce a guide-point definition and propose an optimal path-searching method for dynamic programming (DP). Second, a distance-based adaptive-filtering method is added to remove noise points and outliers around the target so that the environmental noise interference in practical environments is reduced before registration. Third, an adaptive-sampling method based on distance is proposed to extract the key points of the target point-cloud, and finally a target surface model based on a hash table is established as an offline matching data structure of point-cloud registration to improve the matching speed and accuracy of the 3-D pose calculation. A series of experiments showed the translation error of the proposed method was less than 1 cm relative to the measurement of 100 cm, and the rotation error was less than 2° relative to the measurement of 180°. Compared with traditional point-cloud registration algorithms, our method exhibited higher accuracy and stability, indicating great potential for use in unstructured environments.

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