Novel automated methods for coarse and fine registrations of point clouds in high precision metrology

Rantoson, Rindra; Nouira, Hichem; Anwer, Nabil; Anwer, Nabil

doi:10.1007/s00170-015-7131-1

Cited by 18 publications

(4 citation statements)

References 30 publications

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“…If the measured surface includes certain distinctive geometry elements such as holes, slots, or pockets, these features can be adopted to align the CAD model and the measurement data to obtain accurate registration. 5 The registration and positioning of blade models is a key step in blade shape detection and analysis. Only by ensuring the reliability of blade model registration and positioning can the reliability of blade forming accuracy result be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

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Research on blade statistics measurement registration planning technology

Zhang

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

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Blade has the characteristics of high machining quality of complex curved surface. If there is no benchmark before machining, it is impossible to judge whether the blades before machining are qualified. Therefore, it is necessary to analyze the blade measurement data. Due to the large measurement point error and disordered distribution, it is necessary to optimize the blade registration. Therefore, blade model registration and positioning is particularly important in blade shape detection and analysis. First, preregistration is carried out based on the six point optimization selection of the blade. After preregistration, the selection method of registration control point set based on theoretical model and statistical error is proposed, planning the registration datum point set on the blade model. The registration control point set is obtained through the registration operation between the measurement data and the registration reference point set. Finally, based on the stability and reliability of important sampling sensitivity and statistical error, obtain the probability density function of error normal distribution statistics samples and important samples. The selection of statistical control points and the rationality of the objective function were verified. The stability/reliability of the statistical alignment point selection is proved to be feasible. The statistical registration deviation is [0.015,0.026] mm, and the ICP registration deviation is [0.031,0.035] mm. The average deviation of statistics registration is about 0.013 mm smaller than the average deviation of ICP registration. The deviation of statistical sampling points is about 0.0214 mm, and that of traditional sampling points is about 0.0275 mm. The deviation of statistical sampling points is about 0.0061 mm smaller than that of traditional sampling points. It meets the requirements of rapid, high efficiency and high precision measurement for aeroengine blades.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on blade statistics measurement registration planning technology

Zhang

Liu

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…Three-dimensional (3D) measurement has been widely used in quality inspection, surface reconstruction and other fields [1][2][3][4]. In surface inspection, scanned data points and the computer-aided design (CAD) model are in different coordinate systems.…”

Section: Introductionmentioning

confidence: 99%

A robust registration algorithm of point clouds based on adaptive distance function for surface inspection

Ding

Liu

Sun

2019

Meas. Sci. Technol.

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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.

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“…In objects of regular size, coarse registration is usually supported by placing markers on their surface [3] or using geometric characteristics of the acquired point cloud data. Methods based on these approaches include curvature [4], normal vector [5], and descriptions of characteristics in high dimensions, such as shape [6], contour curves [7], 3D local feature descriptors [8], and others [9]. In addition, the extended Gaussian image and Fourier transform have been used to perform coarse registration [10].…”

Section: Introductionmentioning

confidence: 99%

Registration Algorithm for Point Cloud Based on Normalized Cross-Correlation

Huang

2019

IEEE Access

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We propose a registration algorithm based on neighborhood similarity for 3D point clouds collected by optical measurement and without prior information. The algorithm first applies the improved minimum spanning tree (Prim algorithm) to classify the point cloud in order to obtain the topology information of the data. Specifically, vectors among root nodes and child nodes are processed, and the points on nodes are classified into different levels according to their scanning angle to simplify data and preserve the most representative points. Then, through the perspective conversion between 2D and 3D and according to the corresponding point set obtained by previous classification, the fast normalized cross-correlation (a 2D matching criterion) is applied to determine the relationship between initial characteristic points. Finally, distance constraints remove the errors between point pairs and allow calculating the registration parameters. Experimental results show that the algorithm has high registration accuracy and is suitable for point cloud data obtained by laser and structured light acquisition. INDEX TERMSMinimum spanning tree, normalized cross-correlation, optical measurement, point cloud classification, point cloud registration. YUAN HUANG received the B.Sc. degree from Nanchang University, in 2010, and the master's degree from the Galway-Mayo Institute of Technology, in 2012. He is currently pursuing the Ph.D. degree with the School of Automation, Southeast University. His main research interests include 3D measurement and image processing.FEIPENG DA received the Ph.D. degree, in 1998. He is currently a Professor and a Ph.D. Supervisor with the School of Automation, Southeast University. His research interests include surface reconstruction, intelligent control, and computer visualization.

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Novel automated methods for coarse and fine registrations of point clouds in high precision metrology

Cited by 18 publications

References 30 publications

Research on blade statistics measurement registration planning technology

Research on blade statistics measurement registration planning technology

A robust registration algorithm of point clouds based on adaptive distance function for surface inspection

Registration Algorithm for Point Cloud Based on Normalized Cross-Correlation

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