Place recognition system from long-term observations

In this paper, we propose a place recognition system which recognize places from a large set of images obtained over time. A set or a sequence of images provides more information about the places and that can be used for more robust recognition. For this, the proposed system recognize places using density matching between the estimated density of the input set and density of the stored images for each place. In the proposed system, we use global texture feature vector for image representation and their density for place recognition. We use a method based on a Gaussian model of texture vector distribution and a matching criterion using the Kullback-Leibler divergence measure. In the experiment, the system successfully recognized the places in several image sequence, the success rate of place recognition was 87% on average.