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A statistical method for geometry inspection from point clouds
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DOI：10.1016/j.amc.2014.05.130.png)
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Abstract
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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. (C) 2014 Elsevier Inc. All rights reserved.
Keywords:
Bootstrap
Kernel smoothing
Inspection
Point cloud
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