Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

Abstract Factor graph optimization (FGO) recently has attracted attention as an alternative to the extended Kalman filter (EKF) for GNSS-INS integration. This study evaluates both loosely and tightly coupled integrations of GNSS code pseudorange INS measurements real-time positioning, using conventional EKF FGO with a dataset collected in urban canyon Hong Kong. The strength is analyzed by degenerating FGO-based estimator into “EKF-like estimator.” In addition, effects window size on performance are evaluated considering error models environmental conditions. We conclude that outperforms because following two factors: (1) uses multiple iterations during estimation achieve robust estimation; (2) better explores time correlation between states, based batch historical data, when do not follow Gaussian noise assumption.