TY - JOUR
T1 - Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter
AU - Gu, Tianqi
AU - Luo, Zude
AU - Tang, Dawei
AU - Chen, Jianxiong
AU - Lin, Shuwen
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Natural Science Foundation of China (Grant No. 51605094) and the Fundamental Research Funds for the Central Universities (Grant No. WK2090050042).
Publisher Copyright:
© IMechE 2022.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - The moving least squares (MLS) and moving total least squares (MTLS) are two of the most popular methods used for reconstructing measurement data, on account of their good local approximation accuracy. However, their reconstruction accuracy and robustness will be greatly reduced when there are outliers in measurement data. This article proposes an improved MTLS method (IMTLS), which introduces an improved random sample consensus (RANSAC) algorithm and a correction parameter in the support domain, to deal with the outliers and random errors. Based on the nodes within the support domain, firstly the improved RANSAC is used to generate a model for establishing the group of pre-interpolation and calculating the residual of each node. Subsequently, the abnormal degree of the node with the largest residual is evaluated by the correction parameter associated with the node residual and random errors. The node with certain abnormal degree will be eliminated and the remaining nodes are used to obtain the approximation coefficients. The correction parameter can be used for data reconstruction without insufficient or excessive elimination. The results of numerical simulation and measurement experiment show that the reconstruction accuracy and robustness of the IMTLS method is superior to the MLS and MTLS method.
AB - The moving least squares (MLS) and moving total least squares (MTLS) are two of the most popular methods used for reconstructing measurement data, on account of their good local approximation accuracy. However, their reconstruction accuracy and robustness will be greatly reduced when there are outliers in measurement data. This article proposes an improved MTLS method (IMTLS), which introduces an improved random sample consensus (RANSAC) algorithm and a correction parameter in the support domain, to deal with the outliers and random errors. Based on the nodes within the support domain, firstly the improved RANSAC is used to generate a model for establishing the group of pre-interpolation and calculating the residual of each node. Subsequently, the abnormal degree of the node with the largest residual is evaluated by the correction parameter associated with the node residual and random errors. The node with certain abnormal degree will be eliminated and the remaining nodes are used to obtain the approximation coefficients. The correction parameter can be used for data reconstruction without insufficient or excessive elimination. The results of numerical simulation and measurement experiment show that the reconstruction accuracy and robustness of the IMTLS method is superior to the MLS and MTLS method.
KW - Measurement data
KW - outliers
KW - moving total least squares
KW - random sample consensus
KW - surface reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85125961553&partnerID=8YFLogxK
U2 - 10.1177/09544054221081330
DO - 10.1177/09544054221081330
M3 - Article
VL - 236
SP - 1589
EP - 1600
JO - Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
JF - Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
SN - 0954-4054
IS - 12
ER -