  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.
