Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin
This work addresses the problem of reliable navigation for systems in urban and signal-compromised environments, representing an incremental improvement over existing tightly coupled fusion methods.
The paper tackled robust positioning in GNSS-challenged environments by developing an adaptive factor graph-based fusion framework that integrates GNSS pseudorange with IMU data using Barron loss, reducing positioning errors by up to 41% compared to standard factor graph optimization.
Reliable positioning in GNSS-challenged environments remains a critical challenge for navigation systems. Tightly coupled GNSS/IMU fusion improves robustness but remains vulnerable to non-Gaussian noise and outliers. We present a robust and adaptive factor graph-based fusion framework that directly integrates GNSS pseudorange measurements with IMU preintegration factors and incorporates the Barron loss, a general robust loss function that unifies several m-estimators through a single tunable parameter. By adaptively down weighting unreliable GNSS measurements, our approach improves resilience positioning. The method is implemented in an extended GTSAM framework and evaluated on the UrbanNav dataset. The proposed solution reduces positioning errors by up to 41% relative to standard FGO, and achieves even larger improvements over extended Kalman filter (EKF) baselines in urban canyon environments. These results highlight the benefits of Barron loss in enhancing the resilience of GNSS/IMU-based navigation in urban and signal-compromised environments.