Interacting multiple-model-driven robust adaptive factor graph optimization for GNSS/SINS/ODO positioning

Integrated navigation systems often have limited awareness of environmental changes in complex settings. When global navigation satellite system (GNSS) signals are disrupted, mismatch in the measurement-noise model can substantially degrade positioning accuracy and stability. To address this problem, we propose an interacting multiple-model-driven innovation-adaptive factor graph optimization method (IMM-IAFGO). First, the IMM framework runs multiple noise models in parallel and updates their posterior probabilities using measurement innovations and prior knowledge, thereby estimating the measurement-noise covariance. The resulting estimate is then used to adaptively reconstruct the observation-information weights of the GNSS factor nodes in the factor graph. Second, robust estimation is achieved through a dynamic adjustment mechanism based on inter-frame innovations. Under severe GNSS signal distortion, odometry (ODO) aided by inertial measurement unit attitude feedforward is introduced to constrain the inter-frame displacement increments. Real-world vehicle experiments demonstrate that the proposed method significantly outperforms error-state Kalman filter, factor graph optimization, and adaptive factor graph. Specifically, it reduces the horizontal positioning root mean square error by 87.92%, 82.85%, and 23.72%, and the mean absolute error by 90.68%, 82.33%, and 26.38%, respectively. This method provides an effective strategy for achieving continuous and high-precision positioning in vehicle integrated navigation systems operating in complex environments.