基于密度比例增长一致性原理的车身构件点云分割算法  

Point Cloud Segmentation Algorithm for Vehicle Body Components Based on Principle of Density-Proportional Growth Consistency

作　　者：廖泽航 贺敏琦 吴浩[1,2] 夏婉扬 王中任 朱大虎 Liao Zehang;He Minqi;Wu Hao;Xia Wanyang;Wang Zhongren;Zhu Dahu(Hubei Longzhong Laboratory,Xiangyang Demonstration Zone,Wuhan University of Technology,Xiangyang 441000,Hubei,China;School of Automotive Engineering,Wuhan University of Technology,Wuhan 430070,Hubei,China;School of Mechanical Engineering,Hubei University of Arts and Science,Xiangyang 441053,Hubei,China)

机构地区：[1]武汉理工大学襄阳示范区湖北隆中实验室,湖北襄阳441000 [2]武汉理工大学汽车工程学院,湖北武汉430070 [3]湖北文理学院机械工程学院,湖北襄阳441053

出　　处：《激光与光电子学进展》2024年第14期209-218,共10页Laser & Optoelectronics Progress

基　　金：湖北隆中实验室自主创新项目(2022ZZ-27)。

摘　　要：针对现有算法在车身构件点云分割过程中存在微小型面结构过分割,孔洞边缘、区域平缓过渡区域欠分割的问题,提出一种密度比例增长一致性(DPGC)分割算法,用于实现复杂型面构件点云的精确分割。该算法以密度比例增长一致性原理为核心,具体过程为:首先,对扫描点云数据进行主成分分析,计算标准密度点云,得到算法的密度比例基准;其次,通过建立自适应点云搜索半径函数模型,获得各区域最佳近邻搜索半径,提高算法对不同特征区域的分割准确性;然后,采用自适应半径密度分割算法,通过设置点云与主投影点云的密度比例阈值初步筛选平面区域;最后,采用等比例自适应半径密度分割算法,通过计算搜索半径内局部投影点云,并以原点云与局部投影点云的密度比值及其等比例缩放区域密度比值的关系为分割依据,对非平面区域进行进一步筛选,得到最终分割结果。实验结果表明,以汽车车门框为典型代表,DPGC分割算法具有更高交并比,在车门框强特征区域的分割效果优于RANSAC-LS和改进区域增长分割算法等主流算法,能够有效实现对车身构件点云的准确分割。To address the problems of oversegmentation of microsmall plane structures and undersegmentation of hole edges and smooth transition regions during the point cloud segmentation for vehicle body components in existing algorithms,a densityproportional growth consistency(DPGC)segmentation algorithm is proposed in this study.This algorithm is used to accurately segment complex surfacecomponent 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 standarddensity point cloud,thereby establishing the algorithm’s densityproportional benchmark;second,devising an adaptive pointcloud 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 densityratio threshold between the point cloud and principalprojection point cloud;and finally,implementing an equalscale adaptive radius density segmentation algorithm to compute the localprojection point cloud within the search radius.The segmentation is based on the density ratio between the localprojection point cloud and original cloud as well as the density ratio of the equalscale 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 RANSACLSand 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.

关 键 词：三维点云 点云分割 投影点密度 车身构件 机器人视觉测量 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]