Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin

TL;DR

The paper tackles robust GNSS/IMU positioning in urban GNSS-challenged environments by deriving an adaptive tightly coupled fusion framework that directly fuses GNSS pseudorange with IMU preintegration. It introduces RFGO, which uses the Barron general robust loss to adaptively down-weight outliers within a factor-graph optimization, enabling seamless handling of non-Gaussian noise without manual tuning. Implemented as an extension of GTSAM and evaluated on UrbanNav, RFGO demonstrates substantial improvements in positioning accuracy and resilience over standard FGO and EKF baselines, particularly in GNSS-denied or degraded regions. The work highlights the practical impact of adaptive robust optimization for resilient navigation and outlines avenues for real-time deployment and sensor augmentation.

Abstract

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.

Figures (13)

• Figure 1: State estimation in sensor fusion: filter-based vs. optimization-based.
• Figure 2: Illustration of the coordinate frames used in this paper; ECEF $(\cdot)^\mathrm{e}$, ENU $(\cdot)^\mathrm{n}$, and World Geodetic System (WGS84) frames.
• Figure 3: Graph structure of the implemented loosely coupled and tightly coupled GNSS/IMU integration based on FGO.
• Figure 4: Flowchart of the implemented loosely coupled/tightly coupled GNSS/IMU integration based on FGO.
• Figure 5: The general Barron loss function (top) and its gradient (bottom) for different values of its shape parameter $\alpha$.