As an essential processing step in unordered picking tasks,point cloud segmentation directly impacts the subsequent accuracy of object recognition and pose estimation.To address the problem of inadequate segmentation per-formance of the traditional LCCP algorithm in complex object stacking scenarios,an improved LCCP point cloud seg-mentation algorithm that incorporates Gaussian curvature information is proposed in this paper.Initially,an enhanced VCCS algorithm is employed to partition the point cloud into super-voxel,and by integrating Gaussian curvature infor-mation,the issue of super-voxel easily crossing object boundaries is further addressed.Subsequently,concave-convex connectivity among adjacent super-voxel blocks is determined,followed by the merging of all convexly connected su-per-voxel to form the final segmentation results.The experimental results demonstrate that the method improves seg-mentation precision by 3.1％to 22％compared to LCCP and CPC,with a noticeable enhancement in overall algo-rithm performance.
point cloud segmentationGaussian curvaturesuper-voxelconcave-convex connectivity
维普
万方数据
