Optimisation of camera positions for optical coordinate measurement based on visible point analysis

Zhang, Hui; Eastwood, Joe; Isa, Mohammed; Sims-Waterhouse, Danny; Leach, Richard; Piano, Samanta

Abstract

In optical coordinate measurement using cameras, the number of images, and positions and orientations of the cameras, are critical to object accessibility and the accuracy of a measurement. In this paper, we propose a technique to optimise the number of cameras and the positions of these cameras for the measurement of a given object using visible point analysis of the object's computer aided design data. The visible point analysis technique is based on a hidden point removal approach; this technique is used to detect which surface points on the object are visible from a given camera position. A genetic algorithm is used to find the set of positions that provide optimum surface point density and overlap between views, while minimising the total number of camera images required. The genetic algorithm is used to minimise the measurement data processing time while maintaining optimum surface point density. We test this optimisation procedure on four artefacts and the measurements are shown to be comparable to that from a traceable contact co-ordinate measurement machine. We show that using our procedure improves the measurement quality compared to the more conventional approach of using equally spaced images. This work is part of a larger effort to fully automate and optimise optical coordinate measurement techniques.

Citation

Zhang, H., Eastwood, J., Isa, M., Sims-Waterhouse, D., Leach, R., & Piano, S. (2021). Optimisation of camera positions for optical coordinate measurement based on visible point analysis. Precision Engineering, 67, 178-188. https://doi.org/10.1016/j.precisioneng.2020.09.016

• Journal Article Type: Article
• Acceptance Date: Sep 24, 2020
• Online Publication Date: Sep 28, 2020
• Publication Date: 2021-01
• Deposit Date: Mar 24, 2021
• Publicly Available Date: Sep 29, 2021
• Journal: Precision Engineering
• Print ISSN: 0141-6359
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 67
• Pages: 178-188
• DOI: https://doi.org/10.1016/j.precisioneng.2020.09.016
• Keywords: General Engineering
• Public URL: https://nottingham-repository.worktribe.com/output/4958949
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0141635920305171?via%3Dihub