Curve and surface reconstruction based on MTLS algorithm combined with k-means clustering

作     者：Gu, Tianqi Lin, Hongxin Tang, Dawei Lin, Shuwen Luo, Tianzhi 

作者机构：Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Univ Sci & Technol China Dept Modern Mech CAS Key Lab Mech Behav & Design Mat Hefei 230022 Peoples R China Univ Huddersfield Ctr Precis Technol Huddersfield HD1 3DH W Yorkshire England 

出 版 物：《MEASUREMENT》 (测量)

年 卷 期：2021年第182卷

页      面：109737-109737页

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China [51605094, 11572316] Thousand Young Talents Program of China Fundamental Research Funds for the Central Universities [WK2090050042] 

主　　题：Surface reconstruction Moving least squares K-means clustering Outliers 

摘      要：Curve and surface reconstruction methods play an important role in many research and engineering fields. It is an imperative procedure to carry out surface reconstruction from measurement data in reverse engineering, which is complicated with the presence of outliers. To achieve better accuracy and robustness of reconstruction, an improved moving total least squares (MTLS) algorithm based on k-means clustering called a KMTLS method is proposed in this article. Based on MTLS, KMTLS adjusts the weight of discrete points within the support domain by adopting a two-step fitting procedure. Firstly, an ordinary least squares (OLS) method is adopted to obtain the pre-fitting result and calculate the residuals as the input of k-means clustering. In k-means clustering, abnormal nodes are classified into one cluster and a weight function based on clustering information is introduced to deal with these nodes. Secondly, based on the compact weight function in MTLS and the weight obtained in the prefitting procedure, a weighted total least squares method is conducted to determine the final estimated value. The process of detecting outliers is automatic without setting threshold artificially. The simulation and experiment show that KMTLS has great robustness and accuracy.