A statistical method for geometry inspection from point clouds

•A statistical method for geometry inspection is proposed.•The null hypothesis states that the real object fits the theoretical.•The shape of the object is estimated by fitting a surface to a point cloud.•The method provides a p-value for the null hypothesis.•A simulation study was designed to evalu...

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Bibliographic Details
Published in:Applied mathematics and computation 2014-09, Vol.242, p.562-568
Main Authors: de Asís López, Francisco, Ordóñez, Celestino, Roca-Pardiñas, Javier, García-Cortés, Silverio
Format: Article
Language:English
Subjects:
Bootstrap
Inspection
Kernel smoothing
Point cloud
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Summary:•A statistical method for geometry inspection is proposed.•The null hypothesis states that the real object fits the theoretical.•The shape of the object is estimated by fitting a surface to a point cloud.•The method provides a p-value for the null hypothesis.•A simulation study was designed to evaluate the performance of the method. This paper introduces a statistical methodology for geometry inspection from point clouds obtained with a laser scanner or other measuring systems. The null hypothesis of interest is that the real surface of an object fits the theoretical shape and dimensions of the object. An algorithm based on bivariate kernel smoothers is used to nonparametrically estimate the surface of the object and bootstrap-based procedures are proposed for testing the null hypothesis. In order to validate the methodology a simulated study was conducted. Finally, the proposed methodology was applied to the inspection of a parabolic dish antenna.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2014.05.130