Outdoor scenes identification on mobile device by integrating vision and inertial sensors

This paper addresses the identification of large scale outdoor scenes on smart phone by fusing outputs of inertial sensors and computer vision techniques. The main contributions can be summarized as follows: Firstly, we propose an overlap region divide (ORD) method to plot image position area, which is fast enough to find the nearest visiting area and can also reduce the search range compared with the traditional approaches. Secondly, the vocabulary tree based approach is improved by introducing fast geometric consistency constraints (FGCC). Our method involves no operation in the high-dimensional feature space and does not assume a global transform between a pair of images. Thus, it substantially reduces the computational complexity and memory usage, which makes the city scale image recognition feasible on the smartphone. Experiments on a collected database including 0.16 million images show that the proposed method demonstrates excellent identification performance, while maintaining the average identification time of less than 1s.