Experimental evaluation of visual place recognition algorithms for personal indoor localization

The paper presents a thorough evaluation of two representative visual place recognition algorithms that can be applied to the problem of indoor localization of a person equipped with a modern smartphone. The evaluation focuses on comparing two different state-of-the-art approaches: single image-based place recognition, represented by the FAB-MAP algorithm, and recognition based on a sequence of images, represented by the ABLE-M algorithm. The evaluation focuses on real-life localization examples in buildings of different structure and the influence of the presence of people in the environment on the recognition results. Moreover, the paper demonstrates feasibility and real-time performance of the visual place recognition methods implemented on an Android smartphone.