Motion-State-Aware Adaptive Multi-Sensor Fusion Localization Using Sliding-Window Incremental Factor Graph Optimization

Accurate and real-time localization for unmanned vehicles in complex motion environments is challenged by asynchronous multi-sensor measurements, time-varying measurement quality, and the growing computational burden of long-term factor graph optimization. To address these problems, this study proposes AVFGO, a sliding-window incremental factor graph optimization framework for motion-state-aware adaptive multi-sensor fusion localization. IMU pre-integration is used as the primary state-propagation backbone, while asynchronous LiDAR odometry, AHRS, wheel odometry, and barometer measurements are uniformly represented as factor-graph constraints. A sliding-window marginalization mechanism bounds the optimization scale by retaining historical information as prior factors. A motion-state-aware fusion-rate strategy controls the insertion density of external factors, and a residual-driven vector-wise adaptive weighting model adjusts the covariance of heterogeneous sensors in different measurement dimensions. Field experiments on a GNSS-denied wheeled unmanned vehicle dataset show that AVFGO achieves a 3D position RMSE of 0.380 m, reducing the error by 65.36% relative to IFGO and 52.71% relative to SWIFGO. The mean single-step optimization time is 0.0389 s, corresponding to an 11.9× speedup over IFGO and a 75.81% reduction relative to SWIFGO. These results indicate that the proposed framework improves accuracy and real-time performance while remaining limited by the single-platform field-test scope, which is explicitly discussed as a direction for future validation.