A Two-Step Global Alignment Method for Feature-Based Image Mosaicing

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