Resource-efficient and Automated Image-based Indoor Localization

Image-based indoor localization has aroused much interest recently because it requires no infrastructure support. Previous approaches on image-based localization, due to their computation and storage requirements, often process queries at servers. This does not scale well, incurs round-trip delay, and requires constant network connectivity. Many also require users to manually confirm the shortlisted matched landmarks, which is inconvenient, slow, and prone to selection error. To overcome these limitations, we propose a highly automated (in terms of image confirmation after taking images) image-based localization algorithm (HAIL), distributed in mobile devices. HAIL achieves resource efficiency (in terms of storage and processing) by keeping only distinguishing visual features for each landmark, and employing the efficient k-d tree to search for features. It further utilizes motion sensors and map constraints to enhance the localization accuracy without user operation. We have implemented HAIL on Android platforms and conducted extensive experiments in a food plaza and a premium shopping mall. Experimental results show that it achieves much higher localization accuracy (reducing the localization error by more than 20%) and computation efficiency (by more than 40% in time) as compared with the state-of-the-art approaches.