Sextant: Towards Ubiquitous Indoor Localization Service by Photo-Taking of the Environment

Mainstream indoor localization technologies rely on RF signatures that require extensive human efforts to measure and periodically recalibrate signatures. The progress to ubiquitous localization remains slow. In this study, we explore Sextant, an alternative approach that leverages environmental reference objects such as store logos. A user uses a smartphone to obtain relative position measurements to such static reference objects for the system to triangulate the user location. Sextant leverages image matching algorithms to automatically identify the chosen reference objects by photo-taking, and we propose two methods to systematically address image matching mistakes that cause large localization errors. We formulate the benchmark image selection problem, prove its NP-completeness, and propose a heuristic algorithm to solve it. We also propose a couple of geographical constraints to further infer unknown reference objects. To enable fast deployment, we propose a lightweight site survey method for service providers to quickly estimate the coordinates of reference objects. Extensive experiments have shown that Sextant prototype achieves 2-5 m accuracy at 80-percentile, comparable to the industry state-of-the-art, while covering a 150 x 75 m mall and 300 x 200m train station requires a one time investment of only 2-3 man-hours from service providers.