A robust registration algorithm of point clouds based on adaptive distance function for surface inspection. (21st May 2019)

• Record Type: Journal Article
• Authors: Ding, Ji; Liu, Qiang; Sun, Pengpeng
• Is Part Of: Measurement science & technology. Volume 30:Number 7(2019:Jul.)
• Publication Date: 2019-05-21
• Subjects: robust registration -- M-estimation -- iteratively reweighted least squares -- adaptive distance function -- surface inspection; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ab16ad
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: One key issue in the optical measurement of free-form or complex surfaces is the point cloud registration procedure, which aligns the measurement data to the part model for a robust, fast and accurate inspection process. Therefore, a robust registration method for surface inspection is proposed based on an adaptive distance function (ADF) and the M-estimation method. The ADF as the basis error metric can accurately describe the shortest point-surface distance, and the M-estimation method is used to eliminate outliers and enhance the robustness of the registration performance. The registration problem using the M-estimation method can be interpreted as an iterative reweighted least squares (IRLS) minimization. Then, a nonlinear optimization model called IRLS-ADF is established to obtain the transformation parameters. The convergence of the proposed method is also analysed. Moreover, compared to the previous algorithms, the experiments confirm that the proposed method can achieve a combination of good robustness, fast convergence speed and high accuracy.