Indoor positioning using visual and inertial sensors

Indoor positioning system is a rapidly emerging technology. Unlike outdoor positioning, which uses triangulation from satellites in line-of-sight, current indoor positioning methods attempt triangulation using Received Signal Strength Indicator (RSSI) from indoor transmitters, like WiFi and RFID. These methods, however, are not accurate and suffer from issues like multi-path and absorption by walls and other objects. In this paper we propose an alternate and novel approach to indoor positioning, that combines signals from multiple sensors. In particular, we focus on visual and inertial sensors that are ubiquitously found in mobile devices. We utilize a Building Information Model (BIM) of the indoor environment as a guideline for navigable paths. The sensor suite signals are processed to generate a trajectory of device moving through the indoor environment. We compute features on this trajectory in real-time and data mine pre-computed features on BIM's navigable paths to determine the location of the device in real-time. We demonstrate our approach on BIM in our university campus. The key benefit of our approach is that unlike previous methods that require installation of a wireless sensor network of several transmitters spanning the indoor environment, we only require a floor-plan BIM and cheap ubiquitous sensor suite on board a mobile device for indoor positioning.