Factor Graph Optimization for Flexibly Modeled INS/GPS Navigation in Graphical State-Space

This article investigates loosely coupled inertial navigation system/global positioning system (INS/GPS) integration for land vehicle navigation. To achieve navigation with higher accuracy and lower computational complexity, we present an integration solution using factor graph optimization (FGO) based on the graphical state-space model (GSSM). This solution is referred to as GSSM-FGO. Compared with traditional methods, the unique specialty of our work lies in both modeling and problem-solving aspects under the assumption of calibration parameter invariance. Specifically, we suggest that the time-series state-space model is not always suitable for widely existing constant calibration parameters. Thus, we propose GSSM as a more flexible and accurate state description by extracting the constant states as singular nodes. The FGO is adopted to manage this novel graphical model, while traditional filter-based algorithms fail when faced with the cyclic model structure. The universality of our approach is validated through a real-world land vehicle navigation dataset, featuring four distinct-grade inertial measurement units. Compared to the methods based on extended Kalman filter and FGO with the traditional state-space model, our approach demonstrates a substantial enhancement in estimation accuracy and computational speed.