中国光学（中英文）2024，Vol.17Issue(5)：1112-1124,13.DOI:10.37188/CO.2023-0225
面向机械零件三角网格模型自动配准中增强特征的分割方法
Segmentation method for enhanced features in automatic registration of triangular mesh model of mechanical parts
摘要
Abstract
Triangular mesh model registration is an important part of industrial automation detection soft-ware.The registration accuracy has an important influence on mechanical parts'shape and position tolerance.Aiming to solve the problems of low accuracy and poor robustness of the automatic registration of triangular mesh models,we propose a segmentation method for enhanced features in the automatic registration of trian-gular mesh models for mechanical parts.First,the K value of the feature segmentation of the triangular mesh model was determined,and the Laplacian matrix determined the seed points for iterative initialization.Second,the appropriate region shape agent and cost function were used to accelerate the process and per-form multi-source iterative clustering to obtain the intended feature segmentation results.Finally,based on the feature segmentation results of the triangular mesh model,the coarse registration based on the singular value decomposition method was performed,then the fine registration was performed according to the EM-ICP.The experimental results show that the proposed method reduces registration error by 25.2%and shortens the automatic registration time by 62.6%,compared with the traditional feature descriptor coarse re-gistration combined with ICP fine registration method.This effectively improves the accuracy and efficiency of the automatic registration of the triangular mesh model.关键词
三角网格模型/特征分割/种子点选取/分割后处理/自动配准Key words
triangular mesh model/feature segmentation/seed point selection/segmentation post-pro-cessing/automatic registration分类
信息技术与安全科学引用本文复制引用
巫志辉,王立忠,梁晋,龚春园,朱峰,常志文,徐建宁..面向机械零件三角网格模型自动配准中增强特征的分割方法[J].中国光学（中英文）,2024,17(5):1112-1124,13.基金项目
国家重点研发计划项目(No.2022YFB4601802) （No.2022YFB4601802）
国家自然科学基金资助项目(No.52275543) Supported by the National Key R&D Program of China(No.2022YFB4601802) （No.52275543）
National Natural Science Foundation of China(No.52275543) （No.52275543）
