Neural Inertial Localization

• URL: http://arxiv.org/abs/2203.15851v1
• Date: Tue, 29 Mar 2022 18:45:27 GMT
• Title: Neural Inertial Localization
• Authors: Sachini Herath, David Caruso, Chen Liu, Yufan Chen, Yasutaka Furukawa
• Abstract summary: 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 uses a neural inertial navigation technique to turn sensor history to a sequence of velocity vectors. Our approach is significantly faster and competitive results even compared with state-of-the-art methods that require a floorplan and run 20 to 30 times slower.
• Score: 24.854242481051383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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