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.

翻译：全球导航卫星系统(GNSS)是为自主系统提供全球参照定位的最受欢迎的来源之一,但是,由于信号反射和从建筑物中阻断,在城市峡谷中全球导航卫星系统定位的性能受到很大挑战,鉴于全球导航卫星系统的测量高度依赖环境,而且与时间有关,全球导航卫星系统定位的常规过滤法不能同时探索历史测量之间的时间关系,因此,基于过滤的估测器对意外外向测量十分敏感,在本文件中,我们为全球导航卫星系统定位和实时运动(RTK)提出了一个基于要素的图表的配方。拟订的参数图表框架有效地探索了假射线、载波波和多普勒测量的时间关系,并导致对全球导航卫星系统接收器的非最低状态估计。与基于过滤的测算器相比,对拟议方法的可行性进行了评估,评估使用了在挑战性城市峡湾中收集的数据集,并取得了与基于过滤的测算器相比,定位精确度有了显著提高。