Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones

TL;DR

This paper enhances smartphone inertial navigation by using iterative estimation methods to refine position estimates, especially when GNSS signals are unreliable, demonstrated through real-world smartphone and tablet data.

Contribution

It introduces iterative estimation techniques for improving large-scale inertial navigation on smartphones, combining inertial data with partial GNSS signals in retrospective scenarios.

Findings

Iterative methods improve positioning accuracy in challenging environments.

Global iterated Kalman filtering outperforms some existing schemes.

Practical validation on real-world smartphone and tablet data.

Abstract

Modern smartphones have all the sensing capabilities required for accurate and robust navigation and tracking. In specific environments some data streams may be absent, less reliable, or flat out wrong. In particular, the GNSS signal can become flawed or silent inside buildings or in streets with tall buildings. In this application paper, we aim to advance the current state-of-the-art in motion estimation using inertial measurements in combination with partial GNSS data on standard smartphones. We show how iterative estimation methods help refine the positioning path estimates in retrospective use cases that can cover both fixed-interval and fixed-lag scenarios. We compare estimation results provided by global iterated Kalman filtering methods to those of a visual-inertial tracking scheme (Apple ARKit). The practical applicability is demonstrated on real-world use cases on empirical data acquired from both smartphones and tablet devices.