Large volume metrology is a key enabler of autonomous precision manufacturing. For component positioning, the optical-based metrology technique of photogrammetry could be used more widely if its accuracy was improved. These positional measurements are subject to uncertainties which can be greater than manufacturing tolerances. One source of uncertainty is due to thermal gradients, which cause the refraction of the light rays in large-scale industrial environments. This paper uses light-based sensor data to reconstruct a heterogeneous spatial map of the refractive index in air. We use this reconstructed refractive index map to discount the refractive effects and thereby reduce the uncertainty of this positioning problem. This new inverse problem employs Voronoi tessellations to spatially parameterize the refractive index map, the Fast Marching Method to solve the forward problem of calculating the light rays through this medium, and a Bayesian approach in the inversion. Using simulated data, this methodology leads to positioning improvements of up to 37$\mathrm{\%}$.

大容量计量是自主精密制造的关键推动力。对于部件定位，如果提高其精度，可以更广泛地使用基于光学的摄影测量技术。这些位置测量受到可能大于制造公差的不确定性的影响。不确定性的来源之一是热梯度，它会导致光线在大规模工业环境中发生折射。本文使用基于光的传感器数据来重建空气中折射率的异构空间图。我们使用这个重建的折射率图来折扣折射效应，从而减少这个定位问题的不确定性。这个新的逆问题采用 Voronoi 镶嵌来对折射率图进行空间参数化，快速行进法解决了计算通过这种介质的光线的正向问题，以及反演中的贝叶斯方法。使用模拟数据，该方法可将定位改进多达 37$\mathrm{\%}$.