A Reconstruction–Registration Integrated Data Fusion Method for Measurement of Multiscaled Complex Surfaces

Ren, Ming; Sun, Lijian; Liu, Ming Yu; Cheung, Chi Fai; Yin, Yue

doi:10.1109/tim.2016.2636538

Cited by 18 publications

(3 citation statements)

References 16 publications

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“…It uses an incomplete polynomial for function fitting, in which the polynomial is composed of a correlation coefficient vector and a polynomial basis function. In addition, a compact weight function is introduced to separate the support domain and assign weights of points [7,8], which makes MLS conduct the localized approximation better. Based on MLS, a variety of construction methods have been derived to solve the problems that cannot be solved by traditional numerical approaches, such as local radial point interpolation method (LRPIM) [9], gaussian radial basis function method (GRBF) [10], diffuse element method (DEM) [11] and reproducing kernel particle method(RKPM) [12], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Profile analysis with reconstruction robustness for measurement data subject to outliers

Xiong

Tang

et al. 2022

Appl. Opt.

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In the surface profile analysis, there are often a few observations that contain outliers. Due to the existence of outliers, the application of non-robust reconstruction algorithms for measurement data will become a huge problem because these methods are often sensitive to outliers and the approximation effectiveness will be greatly aggravated. In view of this, this paper presents a novel angle-based moving total least squares reconstruction method, to the best of our knowledge, that applies two-step pre-treatment to handle outliers. The first step is an abnormal point detection process that characterizes the geometric features of discrete points in the support domain through a new angle-based parameter constructed by total least square. Then, the point with the largest anomaly degree is removed, and a relevant weight function is defined to adjust the weights of the remaining points. After pre-treatment, the final estimates are calculated by weighted total least squares (WTLS) based on the compact weight function. The detection and removal of outliers are automatic, and there is no need to set a threshold value artificially, which effectively avoids the adverse impacts of human operation. Numerical simulations and experiments verify the applicability of the proposed algorithm as well as its accuracy and robustness.

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Section: Introductionmentioning

confidence: 99%

Profile analysis with reconstruction robustness for measurement data subject to outliers

Xiong

Tang

et al. 2022

Appl. Opt.

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“…B-spline model is another developed technique to represent complex local geometry. Ren [19] employed B-spline to reconstruct a measured surface that had removed the residual. Results of the investigation indicate that this method has technological merit in terms of reducing computation cost and fusing complex surfaces.…”

Section: Introductionmentioning

confidence: 99%

A fiducial-aided data fusion method for the measurement of multiscale complex surfaces

Wang

Cheung

Liu

2019

Int J Adv Manuf Technol

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Multiscale complex surfaces possessing high form accuracy and geometric complexity are widely used for various applications in fields such as telecommunications and biomedicines. Despite the development of multi-sensor technology, the stringent requirements of form accuracy and surface finish still present many challenges in their measurement and characterization. This paper presents a fiducial-aided data fusion method (FADFM), which attempts to address the challenge in modelling and fusion of the datasets from multi-scale complex surfaces. The FADFM firstly makes use of fiducials, such as standard spheres, as reference data to form a fiducial-aided computer-aided design (FA-CAD) of the multiscale complex surface so that the established intrinsic surface feature can be used to carry out the surface registration. A scatter-searching algorithm is employed to solve the nonlinear optimization problem, which attempts to find the global minimum of the transformation parameters in the transforming positions of the fiducials. Hence, a fused surface model is developed which takes into account both fitted surface residuals and fitted fiducial residuals based on Gaussian process modelling. The results of the simulation and measurement experiments show that the uncertainty of the proposed method was up to 3.97 x 10 -5 μm based on a surface with zero form error. In addition, there is a 72.5% decrease of the measurement uncertainty as compared with each individual sensor value and there is an improvement of more than 36.1% as compared with the Gaussian process based data fusion technique in terms of rootmean-square (RMS) value. Moreover, the computation time of the fusion process is shortened by about 16.7%. The proposed method achieves final measuring results with better metrological quality than that obtained from each individual dataset, and it possesses the capability of reducing the measurement uncertainty and computational cost.

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“…These point sets can then be transformed into one common reference frame for the 3D model reconstruction [23].…”

Section: Introductionmentioning

confidence: 99%

K-means clustering for efficient and robust registration of multi-view point sets

Jiang,

Zhu,

Evangelidis

et al. 2017

Preprint

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Generally, there are three main factors that determine the practical usability of registration, i.e., accuracy, robustness, and efficiency. In real-time applications, efficiency and robustness are more important. To promote these two abilities, we cast the multi-view registration into a clustering task. All the centroids are uniformly sampled from the initially aligned point sets involved in the multi-view registration, which makes it rather efficient and effective for the clustering. Then, each point is assigned to a single cluster and each cluster centroid is updated accordingly. Subsequently, the shape comprised by all cluster centroids is used to sequentially estimate the rigid transformation for each point set. For accuracy and stability, clustering and transformation estimation are alternately and iteratively applied to all point sets. We tested our proposed approach on several benchmark datasets and compared it with state-of-the-art approaches. Experimental results validate its efficiency and robustness for the registration of multi-view point sets.

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A Reconstruction–Registration Integrated Data Fusion Method for Measurement of Multiscaled Complex Surfaces

Cited by 18 publications

References 16 publications

Profile analysis with reconstruction robustness for measurement data subject to outliers

Profile analysis with reconstruction robustness for measurement data subject to outliers

A fiducial-aided data fusion method for the measurement of multiscale complex surfaces

K-means clustering for efficient and robust registration of multi-view point sets

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