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Accurate and smooth global navigation satellite system (GNSS) positioning forpedestrians in urban canyons is still a challenge due to the multipath effectsand the non-light-of-sight (NLOS) receptions caused by the reflections fromsurrounding buildings. The recently developed factor graph optimization (FGO)based GNSS positioning method opened a new window for improving urban GNSSpositioning by effectively exploiting the measurement redundancy from thehistorical information to resist the outlier measurements. Unfortunately, theFGO-based GNSS standalone positioning is still challenged in highly urbanizedareas. As an extension of the previous FGO-based GNSS positioning method, thispaper exploits the potential of the pedestrian dead reckoning (PDR) model inFGO to improve the GNSS standalone positioning performance in urban canyons.Specifically, the relative motion of the pedestrian is estimated based on theraw acceleration measurements from the onboard smartphone inertial measurementunit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Dopplermeasurements, and relative motion from PDR are integrated using the FGO. Giventhe context of pedestrian navigation with a small acceleration most of thetime, a novel soft motion model is proposed to smooth the states involved inthe factor graph model. The effectiveness of the proposed method is verifiedstep-by-step through two datasets collected in dense urban canyons of Hong Kongusing smartphone-level GNSS receivers. The comparison between the conventionalextended Kalman filter, several existing methods, and FGO-based integration ispresented. The results reveal that the existing FGO-based GNSS standalonepositioning is highly complementary to the PDR's relative motion estimation.Both improved positioning accuracy and trajectory smoothness are obtained withthe help of the proposed method.

Trajectory Smoothing Using GNSS/PDR Integration Via Factor Graph Optimization in Urban Canyons