Point cloud-based 3D round hole detection

Aiming at the limited fitting conditions of existing 3D circle feature extraction algorithms, a 3D circle feature extraction algorithm based on nonparametric estimation is proposed. Firstly, kd-tree is used to establish topological relationship and define the distance threshold. Edge points are extracted by K -nearest neighbor search. Then use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to cluster edge points for calculate the maximum distance between edge points in the cluster. Finally, the optimal interval of the maximum distance value is calculated using nonparametric estimation. The center point is obtained by extracting the circular hole feature points. The degree of dispersion from the edge point in the cluster to the center point is analyzed to determine whether the cluster is a three-dimensional circle feature. The algorithm is verified using the grid assembly printed parts (arch circular hole) and rectangular flange (plane circular hole) as the test workpiece. The results show that the algorithm can effectively extract three-dimensional round hole features.