  Accurate and smooth global navigation satellite system (GNSS) positioning for
pedestrians in urban canyons is still a challenge due to the multipath effects
and the non-light-of-sight (NLOS) receptions caused by the reflections from
surrounding buildings. The recently developed factor graph optimization (FGO)
based GNSS positioning method opened a new window for improving urban GNSS
positioning by effectively exploiting the measurement redundancy from the
historical information to resist the outlier measurements. Unfortunately, the
FGO-based GNSS standalone positioning is still challenged in highly urbanized
areas. As an extension of the previous FGO-based GNSS positioning method, this
paper exploits the potential of the pedestrian dead reckoning (PDR) model in
FGO to improve the GNSS standalone positioning performance in urban canyons.
Specifically, the relative motion of the pedestrian is estimated based on the
raw acceleration measurements from the onboard smartphone inertial measurement
unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler
measurements, and relative motion from PDR are integrated using the FGO. Given
the context of pedestrian navigation with a small acceleration most of the
time, a novel soft motion model is proposed to smooth the states involved in
the factor graph model. The effectiveness of the proposed method is verified
step-by-step through two datasets collected in dense urban canyons of Hong Kong
using smartphone-level GNSS receivers. The comparison between the conventional
extended Kalman filter, several existing methods, and FGO-based integration is
presented. The results reveal that the existing FGO-based GNSS standalone
positioning is highly complementary to the PDR's relative motion estimation.
Both improved positioning accuracy and trajectory smoothness are obtained with
the help of the proposed method.
