Neural Inertial Localization

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...

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Bibliographic Details
Published in2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 6594 - 6603
Main Authors Herath, Sachini, Caruso, David, Liu, Chen, Chen, Yufan, Furukawa, Yasutaka
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
Energy efficiency
Inertial navigation
Inertial sensors
Location awareness
Motion and tracking
Pattern recognition
Soft sensors
Transformers
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Summary: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.
ISSN:2575-7075
DOI:10.1109/CVPR52688.2022.00649