The accuracy and reliability of state estimation are seriously affected by measurement outliers and time-varying noise in complex environments. To address these issues, a robust adaptive factor graph optimization (FGO) integrated navigation algorithm based on variational Bayesian is proposed. First, the variational Bayesian inference is introduced into the FGO framework based on a priori and a posteriori two-stage updating to estimate the time-varying measurement noise covariance. Secondly, the mean innovation between neighboring keyframes is used to construct measurement covariance prediction as an outlier judgment to achieve robust estimation. Simulation and field tests based on INS / GNSS integrated navigation show that the proposed method can effectively estimate the timevarying measurement noise covariance in the presence of outlier interference, and reduce the horizontal position error by 26. 7% and 39. 8% compared to the M-estimation and sliding window adaptive FGO algorithms, which takes into account the accuracy and robust performance. It has an excellent adaptation to complex scenarios.