Abstract Machine vision technology assists welding robot weld positioning, which can significantly improve welding accuracy and efficiency. This paper delves into a 3D point cloud-based positioning technique specifically tailored for structural reinforcement welds, utilizing the high-precision 3D structured light camera to get 3D point cloud data from the surface of the reinforcement structures, subsequently conducting a comprehensive analysis of the weld’s geometric characteristics. When the weld seam position can be directly collected, the side plate plane and the bottom plate plane of the reinforcement can be divided by the random sample consensus method. The weld points with rich curvature characteristics can be quickly and reliably selected according to the plane constraint. When the weld position cannot be measured directly, the prediction of the weld position is realized by extracting the edge of the upper surface of the reinforcing rib in the depth map and combining the plane normal vector of the base plate and the point laser correction vector. The experimental results show that this method can accurately locate the weld seam with high accuracy in both working scenarios.
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