Towards Robust GNSS Positioning and Real-time Kinematic Using Factor Graph Optimization

TL;DR

This paper introduces a factor graph-based approach for GNSS and RTK positioning that effectively models temporal correlations, significantly improving accuracy in urban canyon environments compared to traditional filtering methods.

Contribution

The paper presents a novel factor graph formulation for GNSS positioning that explores time-correlation among measurements, enhancing robustness in challenging urban environments.

Findings

Significantly improved positioning accuracy in urban canyons.

Effective exploitation of measurement time-correlation.

Robustness against outliers and environmental challenges.

Abstract

Global navigation satellite systems (GNSS) are one of the utterly popular sources for providing globally referenced positioning for autonomous systems. However, the performance of the GNSS positioning is significantly challenged in urban canyons, due to the signal reflection and blockage from buildings. Given the fact that the GNSS measurements are highly environmentally dependent and time-correlated, the conventional filtering-based method for GNSS positioning cannot simultaneously explore the time-correlation among historical measurements. As a result, the filtering-based estimator is sensitive to unexpected outlier measurements. In this paper, we present a factor graph-based formulation for GNSS positioning and real-time kinematic (RTK). The formulated factor graph framework effectively explores the time-correlation of pseudorange, carrier-phase, and doppler measurements, and leads to the non-minimal state estimation of the GNSS receiver. The feasibility of the proposed method is evaluated using datasets collected in challenging urban canyons of Hong Kong and significantly improved positioning accuracy is obtained, compared with the filtering-based estimator.