This article proposes an adaptive filtering-collaborative graph optimization navigation method to address the problem of inaccurate sensor measurement covariance in the traditional graph optimization navigation method, which leads to a decrease in estimation accuracy. Firstly, a factor graph model for the INS/GNSS/e-Compass integrated navigation system is established. Then, the adaptive filter is used to pre-estimate the sensor measurement information based on the measurement variance,the measurement covariance matrix of relevant sensors is updated during the filtering process, and thepre-estimated result is added to the factor graph as the variable node. Finally, the sliding window is used to control the optimization range, and the nonlinear optimization of the variable nodes within the sliding window is performed. Thus, the final navigation states are achieved. Simulation and experimental results show that the proposed method has adaptability to the mismatch of sensor measurement covariance and can achieve efficient and reliable navigation positioning in different environments. Compared with the traditional graph optimization method,this method improves the positioning accuracy by 30%and the calculation efficiency by 12%.