Optimization Method of GNSS/INS Factor Graph Using Forward Tightly Coupled
TL;DR
Experiments demonstrate that the proposed forward tightly coupled GNSS/INS factor graph optimization method improves navigation accuracy compared with conventional EKF and FGO, and the comparison of robust weighting models demonstrates that IGG-III provides the best overall three-dimensional positioning performance among the tested models.
Abstract
Although Kalman filtering and factor graph optimization have been widely used in GNSS/INS integrated navigation, their positioning accuracy can degrade significantly in urban canyon environments because of satellite signal blockage, multipath effects, and non-line-of-sight errors. To improve robustness and accuracy, this study proposes a forward tightly coupled GNSS/INS factor graph optimization method. In the front end, raw GNSS and INS measurements are fused in a tightly coupled framework, and an IGG-III robust weighting model is introduced to suppress abnormal observations. In the back end, GNSS position factors, IMU pre-integration factors, and marginalization factors are constructed within a sliding window to optimize the navigation states while preserving historical information. Experiments on an urban canyon dataset demonstrate that the proposed method improves navigation accuracy compared with conventional EKF and FGO. Further analyses show that the adopted slidingwindow configuration reduces the mean complete-epoch processing time by 26.31% while maintaining comparable positioning accuracy. RFGO also exhibits better performance during a 120 s complete GNSS outage, and the comparison of robust weighting models demonstrates that IGG-III provides the best overall three-dimensional positioning performance among the tested models.