Point cloud segmentation method for irregular parts based on Euclidean clustering
Min Guo, Jing Zhang, Yuanzhi Lyu, Zhenlan Li · 2024
In view of the problem of under-segmentation and over-segmentation in the point cloud segmentation process of irregular parts, a point cloud segmentation method for irregular parts based on Euclidean clustering is studied. First, the initial feature point extraction is performed based on the fast point feature histogram (FPFH); then the method of adjacent point angle and projection point angle is used to optimize the initial feature point extraction effect; finally, the threshold parameter is set, and the improved Euclidean clustering algorithm is used for clustering segmentation, and the clustering segmentation effect result graphs are obtained. The experimental results show that the algorithm can improve the segmentation accuracy of the irregular parts point cloud, not only ensure the integrity of each feature, but also enhance the processing of each feature detail part, and improve the speed of the clustering segmentation process.