GNSS Outlier Mitigation Via Graduated Non-Convexity Factor Graph Optimization
This addresses GNSS positioning errors for vehicles in urban environments, representing an incremental improvement over existing methods.
The paper tackles GNSS positioning degradation from outlier measurements like multipath and NLOS by proposing FGO-GNC, a method that uses graduated non-convexity and factor graph optimization to estimate optimal weightings, improving accuracy in urban canyon datasets.
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.