To address the problems of oversegmentation of micro-small plane structures and undersegmentation of hole edges and smooth transition regions during the point cloud segmentation for vehicle body components in existing algorithms,a density-proportional growth consistency(DPGC)segmentation algorithm is proposed in this study.This algorithm is used to accurately segment complex surface-component point clouds by adhering to the principle of DPGC.The specific methodology involves the following steps:first,performing principal component analysis on the scanning point cloud data to calculate the standard-density point cloud,thereby establishing the algorithm's density-proportional benchmark;second,devising an adaptive point-cloud search radius function model to determine the optimal nearest neighbor search radius for each region,enhancing the segmentation accuracy across different feature regions;third,employing an adaptive radius density segmentation algorithm to preliminary screen plane regions using a density-ratio threshold between the point cloud and principal-projection point cloud;and finally,implementing an equal-scale adaptive radius density segmentation algorithm to compute the local-projection point cloud within the search radius.The segmentation is based on the density ratio between the local-projection point cloud and original cloud as well as the density ratio of the equal-scale region,further refining the nonplane regions to achieve the final segmentation result.Results of comparative tests demonstrate that the DPGC segmentation algorithm has a higher intersection over union and surpasses mainstream algorithms such as RANSAC-LS and improved region growth segmentation algorithm,particularly in areas with strong features such as door frames,thereby effectively achieving accurate point cloud segmentation of body components.
3D point cloudpoint cloud segmentationprojection point densityvehicle body componentrobotic vision measurement
NETL
NSTL
万方数据
