结合属性邻接图与点云的零件模型特征识别方法  

A part model feature recognition method combining attribute adjacency graph and point cloud

作　　者：舒敏 杨涛 SHU Min;YANG Tao(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,China;Key Laboratory of Sichuan Province for Robot Technology Used for Special Environment,Mianyang 621010,China)

机构地区：[1]西南科技大学信息工程学院,绵阳621010 [2]特殊环境机器人技术四川重点实验室,绵阳621010

出　　处：《现代制造工程》2024年第12期94-101,共8页Modern Manufacturing Engineering

摘　　要：针对目前基于属性邻接图与点云的零件模型特征识别技术存在的局限性,结合2种特征识别方法提出了一种结合属性邻接图与点云的零件模型特征识别方法。利用模型属性邻接图匹配特征子图找到特征面并分离,再将特征面进行点云采样,最后在PointNet网络基础上改进点云分类网络结构。通过添加局部特征提取模块与基于Transformer网络的非局部特征提取模块,并结合特征属性邻接图信息与原始点云数据,对24种常见特征进行特征识别试验,最终识别准确率为99.92%。A part model feature recognition method combining attribute adjacency graph and point cloud was proposed by combining two feature recognition methods to overcome the limitations of current part model feature recognition technology based on attribute adjacency graph and point cloud.The model attribute adjacency graph was used to match feature subgraphs to find and separate feature surfaces,and then the feature surfaces in point clouds were sampled.The point cloud classification network structure on the basis of PointNet network was improved by adding a local feature extraction module and a Transformer based non-local feature extraction module and combining feature attribute adjacency graph information with original point cloud data.Experimental results indicate that the recognition accuracy for 24 common features is 99.92 %.

关 键 词：零件模型特征识别 属性邻接图 点云 Transformer网络 PointNet 

分 类 号：TP391.7[自动化与计算机技术—计算机应用技术] TH161.1[自动化与计算机技术—计算机科学与技术]