Ceiling mosaics through information-based SLAM

In this paper, we present a novel approach to computing ceiling mosaics based on Information Theory. The only sensor of the robot is a digital camera oriented to the ceiling of the map, which is used to approximate the Simultaneous Localization and Mapping (SLAM) problem. We have divided the algorithm into two steps: (i) action estimation, which approximates the actions of the robot maximizing the Mutual Information between consecutive views; and (ii) global rectification, which rectifies the drift of the global trajectory minimizing the entropy of the map. Moreover, a fisheye lens is used to recover enough information from ceilings, reducing their inherent ambiguity. Such lenses produce a semi-spherical aberration in the images, that must be rectified using some information about calibration. In order to do so, we propose a novel technique for image rectification, also based on Information Theory. Finally, we present some experimental results using real data, that prove the robustness of the method.