At present, the information fusion method of multi-sensor integrated navigation system is based on the known variance of measurement noise, but the variance of measurement noise will change with internal and external interference. Therefore, this paper firstly extends the variational Bayesian approximation based adaptive Kalman filter (VB-AKF) from a single integrated navigation system to a multi-sensor integrated navigation system. Then, two kinds of centralized fusion algorithms of multi-sensor integrated navigation system are proposed, namely, the VB-AKF based augmented centralized fusion algorithm and the VB-AKF based sequential centralized fusion algorithm, to solve the problem of information fusion of multi-sensor integrated navigation with unknown measurement noise variance. Finally, the SINS / GNSS / CNS / ADS multi-sensor integrated navigation system is used to validate the above algorithm. The experimental results show that the two algorithms proposed in this paper have the same filtering accuracy and are close to the ideal centralized Kalman fusion algorithm ( ICKF) when the variance of measurement noise is known. In the whole simulation period, compared with traditional centralized Karl filter (TCKF) and federal Kalman filter (FT-FKF) with fault tolerance function, the proposed algorithm can improve position accuracy by 32% and 90%, and speed accuracy by 38% and 71%, respectively