Weld seam recognition algorithm based on a fast point cloud plane fitting method

Aiming at the problems of low accuracy and slow speed of existing point cloud weld extraction algorithms in 3D vision-based robotic intelligent welding, this study proposes a novel, to our knowledge, three-stage automatic point cloud weld extraction method. In the plane segmentation stage, the random sample consensus (RANSAC) algorithm is improved: by narrowing the selection range of sampling points to the local neighborhood and optimizing neighborhood construction with dynamic curvature detection, the efficiency of plane fitting is enhanced. In the feature point extraction stage, based on plane parameters, the plane intersection line method and distance threshold method are adopted to obtain weld seam feature points. In the curve fitting stage, farthest point sampling (FPS) is used to denoise and resample the feature points, and then the weld curve is fitted to achieve high-precision contour reconstruction. Experiments show that the method exhibits high efficiency, robustness, and engineering adaptability.