Research on point cloud processing of end surface of bundled round steels and positioning method of welding tag point
In order to achieve accurate positioning of welding tag by robot system,a processing method of point cloud of the end surface of bundled round steels and welding point positioning was proposed.Firstly,the traditional filtering methods of point cloud were improved in terms of search rules and parameter.Secondly,combining Euclidean clustering,axis-aligned bounding box(AABB)and reverse K-nearest neighbor(RKNN)search,a point cloud segmentation algorithm was proposed.Then,a welding point selection strategy was formulated based on welding tag requirements.The point cloud position was corrected through normal vectors estimation.The random sample consensus(RANSAC)algorithm was used to fit the seleted point cloud of the end surface of bundled round steels to obtain the welding point position.Finally,a welding tag experiment was conducted.The results show that this method effectively removes the influence of irrelevant point clouds,shortens the filtering time,greatly reduces the number of point cloud,effectively segments the point cloud adhesion,obtains the point cloud for each round steel end face,determines the round steel that meets the welding tag requirements,and accurately obtains the coordinates of the welding points.The welding plate experiment shows that the relative positioning error is less than 8%,which meets the actual production needs.The proposed method is conducive to improve the accuracy and efficiency of welding tag positioning in the welding tag robot system,and can also provide reference for the positioning of similar industrial robot systems.
computer image processingpoint cloudimproved filtering methodRKNN algorithmbundled round steelswelding tag
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