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面向自动修补的圆柱特征孔洞识别
作者姓名:王春香  刘 流  周国勇  纪康辉
作者单位:内蒙古科技大学机械学院,内蒙古 包头 014010
基金项目:包头市科技发展计划项目(2019Z3004-6)
摘    要:对于型面复杂且含有大量孔洞的点云模型,目前逆向软件和修补算法皆存在单孔逐一修补时效 率较低、人机交互过多;多孔同时修复精度不高、特征丢失等问题。因此,有必要实现以孔洞的分类识别为前 提,以特征保持为目标的高效、高精度的点云孔洞分类自动修补方式。基于上述想法,提出一种在孔洞识别的 基础上将圆柱特征孔洞与一般类型孔洞分类的方法。首先,借助最大角度识别准则提取孔洞边界点集合,通过 欧式聚类算法分割并统计孔洞总数,然后利用 RANSAC 算法和设定的距离阈值提取模型中的圆柱特征孔洞。 实验结果表明,该方法不仅可提取模型中直径大小不同的多个圆柱特征孔洞,而且能估算出较为准确的圆柱面 几何参数,实现了面向自动修补技术的一类特征孔洞的识别。

关 键 词:点云模型  孔洞识别  自动修补  聚类算法  RANSAC算法  

Cylindrical feature hole recognition for automatic repair
Authors:WANG Chun-xiang  LIU Liu  ZHOU Guo-yong  JI Kang-hui
Affiliation:School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China
Abstract:For the point cloud model with complex profiles and many holes, the existing reverse software and repair algorithm exhibit such problems as lower efficiency and excessive human-computer interaction in hole-by-hole repairing, as well as low repair accuracy and loss of features in multi-hole repairing. Therefore, it is necessary to develop an efficient and high-precision automatic repair mode aiming for feature preserving based on identification and classification of holes in point clouds. Based on the above ideas, a hole recognition-based method was proposed for separating cylindrical feature holes from general types of holes. First, boundary points of holes were extracted and collected following the criterion of maximum angle recognition; the total number of holes was calculated with Euclidean clustering. After that, cylindrical feature holes in the model were extracted using RANSAC and the set distance threshold. According to experimental results, the method can both extract from the model multiple cylindrical feature holes of different diameters, and make estimate of geometric parameters of the cylindrical surface more accurately. In this way, the automatic repairing-oriented identification of a specific type of feature holes can be achieved. 
Keywords:   point cloud model  hole identification  automatic repair  clustering algorithm  RANSAC algorithm   
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