Modeling of Places Based on Feature Distribution.

In this paper, a place model based on a feature distribution is proposed for place recognition. In many previous proposed methods, places are modeled as images or a set of extracted features. In those methods, a database of images or feature sets should be built. The cost of search time will grow exponentially when the database goes large. The proposed feature distribution method uses global information of each place and the search space grows linearly according to the number of places. In the experiments, we evaluate the performance using different number of frames and features for the recognition each time. Additionally, we have shown that the proposed method is applicable to many real-time applications such as robot navigation, wearable computing systems, and so on. [ABSTRACT FROM AUTHOR]