关键词: 自由曲面零件, 机器人视觉定位, 分块, 质心点, 重叠区域, 关键点

Abstract: Aiming at the problem that the lack of regular features of large free-form parts leaded to high positioning cost,low efficiency and difficult to guarantee accuracy,a rapid robot vision positioning method was proposed.An adaptive resolution supervoxel clustering algorithm was designed to uniformly segment point clouds of different models.Based on the curvature,angle and Fast Point Feature Histogram (FPFH) descriptor of the block centroid points,the overlap area between the complete CAD model point cloud and the local measurement data was identified quickly and the coarse registration matrix was solved.The Branch and Bound (BNB) nested Iterative Closest Point (ICP) algorithm was improved,and a fast fine registration method based on key points was presented.Experimental verification was performed on stamping parts,and the results showed that the proposed method saved more than 30% of the registration time,and significantly improved the registration accuracy,which could solve the positioning problem of large free-form surface parts.

Key words: free-form surface parts, robot vision positioning, division, centroid point, overlapping region, key point