GNSS Outlier Mitigation Via Graduated Non-Convexity Factor Graph Optimization

TL;DR

This paper introduces a novel graduated non-convexity factor graph optimization method to mitigate GNSS outliers, significantly enhancing vehicular positioning accuracy in urban environments with multipath effects.

Contribution

The paper proposes a globally optimized FGO-GNC approach using the Geman McClure function to better estimate measurement weights and reduce outlier impact in GNSS positioning.

Findings

Improved positioning accuracy in urban canyon datasets.

Effective outlier mitigation with non-convex optimization.

Robust performance with low-cost GNSS receivers.

Abstract

Accurate and globally referenced global navigation satellite system (GNSS) based vehicular positioning can be achieved in outlier-free open areas. However, the performance of GNSS can be significantly degraded by outlier measurements, such as multipath effects and non-line-of-sight (NLOS) receptions arising from signal reflections of buildings. Inspired by the advantage of batch historical data in resisting outlier measurements, in this paper, we propose a graduated non-convexity factor graph optimization (FGO-GNC) to improve the GNSS positioning performance, where the impact of GNSS outliers is mitigated by estimating the optimal weightings of GNSS measurements. Different from the existing local solutions, the proposed FGO-GNC employs the non-convex Geman McClure (GM) function to globally estimate the weightings of GNSS measurements via a coarse-to-fine relaxation. The effectiveness of the proposed method is verified through several challenging datasets collected in urban canyons of Hong Kong using automobile level and low-cost smartphone level GNSS receivers.