Neural Inertial Localization

TL;DR

This paper introduces neural inertial localization (NILoc), a novel method for estimating indoor device location solely from inertial sensors using a neural network and transformer architecture, with a new dataset and competitive results.

Contribution

The paper presents a new inertial localization problem, a large inertial dataset, and a neural method that is faster and competitive with existing solutions requiring additional data.

Findings

NILoc achieves competitive accuracy using only IMU sensors.

The method is significantly faster than state-of-the-art approaches.

The dataset enables further research in inertial-based indoor localization.

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.