Neural Inertial Localization

Sachini Herath, David Caruso, Chen Liu, Yufan Chen, Yasutaka Furukawa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 6604-6613


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 velocity estimates. 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

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@InProceedings{Herath_2022_CVPR, author = {Herath, Sachini and Caruso, David and Liu, Chen and Chen, Yufan and Furukawa, Yasutaka}, title = {Neural Inertial Localization}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {6604-6613} }