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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. [ABSTRACT FROM AUTHOR]

Rail-track detection is a core function for automated rail transit perception. However, existing methods cannot effectively detect the rail-tracks in a complex environment, especially in turnout scenarios. In this study, we propose a topology guided method to detect rail-tracks which includes the following four parts: Firstly, a neural network is used to obtain the pixels of the rail-lanes, and the geometric relationship between rail-lanes is mined by inverse perspective transformation. Secondly, the rail-lanes’ pixels are converted to rail-lanes’ key points and the topological relationship between the key points. Thirdly, the rail-lanes are reconnected through topological relationships between key points. Finally, the rail-track geometry features are used to match the rail-lanes. Experimental results show that the rail-track level F1 score of the proposed method reached 91.62%, which is state-of-the-art (SOTA) in this field. Furthermore, the proposed method has been tested and applied on the Hong Kong Metro Tsuen Wan Line. [ABSTRACT FROM AUTHOR]