Neural Inertial Localization
This addresses indoor localization for users needing energy-efficient and privacy-preserving solutions, though it is incremental as it builds on existing inertial navigation techniques.
The paper tackles the problem of estimating absolute location from inertial sensor measurements, presenting a dataset with 53 hours of data and a method that achieves competitive results while being significantly faster than state-of-the-art approaches that require floorplans.
This paper proposes the inertial localization problem, the task of estimating the absolute location from a sequence of inertial sensor measurements. This is an exciting and unexplored area of indoor localization research, where we present a rich dataset with 53 hours of inertial sensor data and the associated ground truth locations. We developed a solution, dubbed neural inertial localization (NILoc) which 1) uses a neural inertial navigation technique to turn inertial sensor history to a sequence of velocity vectors; then 2) employs a transformer-based neural architecture to find the device location from the sequence of velocities. We only use an IMU sensor, which is energy efficient and privacy preserving compared to WiFi, cameras, and other data sources. Our approach is significantly faster and achieves competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower. We share our code, model and data at https://sachini.github.io/niloc.