Accurate and infrastructure-free indoor positioning can be very useful in a variety of applications. However, most existing approaches (e.g., WIFI and infrared based approaches) for indoor localization heavily rely on infrastructures, which is neither scalable nor pervasively applicable. In this paper, we propose a novel indoor localization and tracking approach termed VMag requiring no infrastructure assistance. The user can be localized while just holding a smartphone. To the best of our knowledge, this is the first exploration on fusing geomagnetic and visual sensing for indoor localization. More specifically, we conduct an in-depth study on both the advantageous properties and challenges in leveraging the geomagnetic field and visual images for indoor localization. Based on the studies, we design a context-aware particle filtering framework to track the user, which can maximize the positioning accuracy. We also introduce a neural-network method to extract deep features from measurements for indoor positioning purpose. Extensive experiments have been conducted on four different indoor settings including a laboratory, a garage, a canteen, and an office building. Experimental results demonstrate the superior performance of VMag over the state-of-the-arts on these indoor settings.
IEEE Transactions on Medical Imaging, Issue 99, 2016, doi: 10.1109/TMI.2016.2623357