Ubiquitous Real-Time Geo-Spatial Localization


Rapidly growing technologies like autonomous navigation require accurate geo-localization in both outdoor and indoor environments. GNSS based outdoor localization has limitation of accuracy, which deteriorates in urban canyons, forested region and is unavailable indoors. Technologies like RFID, UWB, WiFi are used for indoor localization. These suffer limitations of high infrastructure costs, and signal transmission issues like multi-path, and frequent replacement of transciever batteries. We propose an alternative to localize an individual or a vehicle that is moving inside or outside a building. Instead of mobile RF transceivers, we utilize a sensor suite that includes a video camera and an inertial measurement unit. We estimate a motion trajectory of this sensor suite using Visual Odometery. Instead of pre-installed transceivers, we use GIS map for outdoors, or a BIM model for indoors. The transport layer in GIS map or navigable paths in BIM are abstracted as a graph structure. The geo-location of the mobile platform is inferred by first localizing its trajectory. We introduce an adaptive probabilistic inference approach to search for this trajectory in the entire map with no initialization information. Using an effective graph traversal spawn-and-prune strategy, we can localize the mobile platform in real-time. In comparison to other technologies, our approach requires economical sensors and the required map data is typically available in the public domain. Additionally, unlike other technologies which function exclusively indoors or outdoors, our approach functions in both environments. We demonstrate our approach on real world examples of both indoor and outdoor locations.

In Proceedings of International Conference on Indoor Spatial Awareness, ACM, CA, USA, November, 2016.