Image mosaicing sits at the core of many optical mapping applications with mobile robotic\nplatforms. As these platforms have been evolving rapidly and increasing their capabilities, the amount\nof data they are able to collect is increasing drastically. For this reason, the necessity for efficient\nmethods to handle and process such big data has been rising from different scientific fields, where\nthe optical data provides valuable information. One of the challenging steps of image mosaicing is\nfinding the best image-to-map (or mosaic) motion (represented as a planar transformation) for each\nimage while considering the constraints imposed by inter-image motions. This problem is referred\nto as Global Alignment (GA) or Global Registration, which usually requires a non-linear minimization.\nIn this paper, following the aforementioned motivations, we propose a two-step global alignment\nmethod to obtain globally coherent mosaics with less computational cost and time. It firstly tries to\nestimate the scale and rotation parameters and then the translation parameters. Although it requires\na non-linear minimization, Jacobians are simple to compute and do not contain the positions of\ncorrespondences. This allows for saving computational cost and time. It can be also used as a fast way\nto obtain an initial estimate for further usage in the Symmetric Transfer Error Minimization (STEMin)\napproach. We presented experimental and comparative results on different datasets obtained by\nrobotic platforms for mapping purposes.
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