Analysis of floor map image in information board for indoor navigation

Various indoor navigation methods have been developed recently, but digitalized data of indoor map is not always available. Therefore, an indoor navigation framework using an image of information board has been proposed. In this method, the process to extract map regions from the image of an information board is necessary to be done by hands beforehand, and the process to estimate passageway regions is important because its information is used in map matching. However, the method of passageway discrimination is very heuristic, which is intended for a specific type of floor maps. Therefore, in this paper, we propose a semi-automatic method to extract map regions from the image of information board with simple user's operation. We use GrabCut method and Snakes method for the extraction method. In GrabCut method, we detect closed regions to prevent the degradation of accuracy when conducting GrabCut to the downsizing image. The proposed method can extract a map region with few deficits in short calculation time. In addition, we propose a machine learning based method to classify passageway regions and other regions from a segment image. We confirmed that the proposed methods are effective and promising by experiments.