A New Reconstruction Method for Measurement Data With Multiple Outliers

Measurement data sometimes contain a few outliers that have a large deviation from the actual value. For such data, the reconstruction results will be greatly distorted around the outliers. To solve this problem, a robust surface approximation method based on moving total least squares (MTLSs) is presented in this article, in which an improved random sample consensus (RANSAC) and classification and regression tree (CART) are introduced into the support domain to detect outliers. First, the residuals of points in the support domain are calculated by the improved RANSAC with an introduced parameter, and then, the abnormal residuals are identified by the CART algorithm. The points corresponding to abnormal residuals are considered as abnormal points. After the abnormal points are eliminated, the remaining data are processed by weighted total least squares (WTLSs) to obtain local fitting parameters. This method can avoid the adverse impact of human intervention and over elimination. The reconstruction results of the numerical simulation and measurement experiment illustrate the effectiveness of our method.