Neural Ranging Inertial Odometry

TL;DR

This paper presents a neural fusion framework combining graph attention UWB networks and recurrent inertial networks for accurate, calibration-free positioning in challenging environments like tunnels, outperforming traditional methods.

Contribution

It introduces a novel neural fusion approach that learns scene-relevant ranging patterns and adapts to various anchor configurations without calibration.

Findings

Outperforms traditional UWB and inertial methods in challenging environments.

Effective in scenarios with limited anchors or outside the convex envelope.

Validated on diverse indoor, outdoor, and tunnel datasets.

Abstract

Ultra-wideband (UWB) has shown promising potential in GPS-denied localization thanks to its lightweight and drift-free characteristics, while the accuracy is limited in real scenarios due to its sensitivity to sensor arrangement and non-Gaussian pattern induced by multi-path or multi-signal interference, which commonly occurs in many typical applications like long tunnels. We introduce a novel neural fusion framework for ranging inertial odometry which involves a graph attention UWB network and a recurrent neural inertial network. Our graph net learns scene-relevant ranging patterns and adapts to any number of anchors or tags, realizing accurate positioning without calibration. Additionally, the integration of least squares and the incorporation of nominal frame enhance overall performance and scalability. The effectiveness and robustness of our methods are validated through extensive experiments on both public and self-collected datasets, spanning indoor, outdoor, and tunnel environments. The results demonstrate the superiority of our proposed IR-ULSG in handling challenging conditions, including scenarios outside the convex envelope and cases where only a single anchor is available.