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Evaluating parametric uncertainty using non-linear regression in fringe projection

Gayton, George; Isa, Mohammed; Leach, Richard K.

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Authors

George Gayton



Abstract

Optical coordinate measurement systems, such as fringe projection systems, offer fast, high-density measurements of arbitrary surface topographies. The versatility, speed and information density of fringe projection measurements make them attractive as in-situ measurement devices and autonomous inspection systems. However, the complex nature of the measurement process makes evaluating uncertainty from a fringe projection measurement complex – even in the hypothetical simple case where the accuracy of a measurement is limited only by the accuracy in the quantities that define a measurement from an indication; named system parameters here. In this paper, by validating a series of assumptions, a method to explore the uncertainty in the system parameters of a fringe projection system is given. The results of this investigation imply the common distortion model (the Brown-Conrady model) is not specific enough to the camera or projector of a fringe projection system to evaluate its uncertainty.

Citation

Gayton, G., Isa, M., & Leach, R. K. (2023). Evaluating parametric uncertainty using non-linear regression in fringe projection. Optics and Lasers in Engineering, 162, Article 107377. https://doi.org/10.1016/j.optlaseng.2022.107377

Journal Article Type Article
Acceptance Date Nov 6, 2022
Online Publication Date Dec 2, 2022
Publication Date 2023-03
Deposit Date Dec 11, 2022
Publicly Available Date Dec 15, 2022
Journal Optics and Lasers in Engineering
Print ISSN 0143-8166
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 162
Article Number 107377
DOI https://doi.org/10.1016/j.optlaseng.2022.107377
Keywords Electrical and Electronic Engineering; Mechanical Engineering; Atomic and Molecular Physics, and Optics; Electronic, Optical and Magnetic Materials
Public URL https://nottingham-repository.worktribe.com/output/14847050
Publisher URL https://www.sciencedirect.com/science/article/pii/S0143816622004274?via%3Dihub

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