Reconstruction of refractive index maps using photogrammetry

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