Neural Augmented Kalman Filters for Road Network assisted GNSS positioning

TL;DR

This paper introduces a novel deep learning approach that combines road network data with GNSS measurements using a neural-augmented Kalman Filter, significantly improving positioning accuracy in urban environments.

Contribution

It presents the first deep learning-based method integrating road network info into a Kalman Filter for GNSS positioning, enhancing robustness and accuracy.

Findings

29% reduction in positioning error in urban scenarios

First deep learning approach combining road data and GNSS for positioning

Validated with real-world data and open-source road networks

Abstract

The Global Navigation Satellite System (GNSS) provides critical positioning information globally, but its accuracy in dense urban environments is often compromised by multipath and non-line-of-sight errors. Road network data can be used to reduce the impact of these errors and enhance the accuracy of a positioning system. Previous works employing road network data are either limited to offline applications, or rely on Kalman Filter (KF) heuristics with little flexibility and robustness. We instead propose training a Temporal Graph Neural Network (TGNN) to integrate road network information into a KF. The TGNN is designed to predict the correct road segment and its associated uncertainty to be used in the measurement update step of the KF. We validate our approach with real-world GNSS data and open-source road networks, observing a 29% decrease in positioning error for challenging scenarios compared to a GNSS-only KF. To the best of our knowledge, ours is the first deep learning-based approach jointly employing road network data and GNSS measurements to determine the user position on Earth.