PeTrack: Smartphone-based Pedestrian Tracking in Underground Parking Lot

Although location awareness is prevalent outdoors due to GNSS systems and devices, pedestrians are back into darkness in indoor buildings such as underground parking lots. Frequently we forget where we park the car and get confused by such maze-like structure. In order to track pedestrians without any additional equipment and map support, we propose PeTrack which is a smartphone-only approach that collects the inertial measurement unit (IMU) data for long-term tracking. Our intuition is to train the tracking model with crowdsourced outdoor trajectories, and infer customized user's trace with only inertial readings at indoors. Specially, we propose an inertial sequence learning framework with outdoor geo-tags. We also exploit opportunistic landmark detection and structure cues to refine the trajectory. We have developed a prototype and conducted experiments in an underground parking lot, and results have shown our effectiveness.