Abstract
Large volume metrology is a key component of autonomous precision manufacturing. Photogrammetry systems are an example of an optical-based metrology system and can be used for component positioning.However,these positional measurements are subject to uncertainties which can be greater than manufacturing tolerances. In large scale industrial environments the uncertainties due to thermal gradients which cause refraction of the light rays, need to be considered. This paper uses light-based sensor data to reconstruct the heterogeneous spatial map of the refractive index in the air. This is then used to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. This new inverse problem employs Voronoi tessellations to spatially parameterise the refractive index map, the forward problem of calculating the light rays through this medium is solved using the Fast Marching Method, and a Bayesian approach is then used as the optimisation method in the inversion. Using simulated data, the recovered refractive index map leads to positioning improvements of up to 37 %.
Original language | English |
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Pages (from-to) | 2696-2718 |
Number of pages | 23 |
Journal | Inverse Problems in Science and Engineering |
Volume | 29 |
Issue number | 13 |
Early online date | 1 Jul 2021 |
DOIs | |
Publication status | E-pub ahead of print - 1 Jul 2021 |
Bibliographical note
Funding Information:This work was funded by a studentship with the University of Strathclyde in collaboration with the National Physical Laboratory (NPL), London, UK.
Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Research Groups and Themes
- Engineering Mathematics Research Group
Keywords
- photogrammetry
- metrology
- refractive index
- reversible jump Markov Chain Monte Carlo
- Fast Marching Method
- Voronoi tessellations