用自适应因子图融合提升导航抗干扰能力
Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin
- 基于因子图框架，融合伪距与惯性预积分数据
- 在城市峡谷中定位误差降低41%，优于传统方法
- 适合高动态、信号弱的复杂环境导航应用
GNSS信号受限环境下可靠定位仍是导航系统的关键挑战。紧耦合GNSS/IMU融合虽能提升鲁棒性，但仍易受非高斯噪声和异常值影响。本文提出一种基于自适应因子图的鲁棒融合框架，直接集成GNSS伪距测量与惯性预积分因子，并引入Barron损失函数——一种通过单一可调参数统一多种m-估计器的通用鲁棒损失。通过自适应降低不可靠GNSS测量的权重，显著增强定位韧性。该方法在扩展GTSAM框架中实现，并在UrbanNav数据集上评估。结果表明，相比标准FGO，定位误差最多降低41%；在城市峡谷环境中，相较扩展卡尔曼滤波（EKF）基线有更显著提升。这些结果凸显了Barron损失在提升城市及信号受损环境下GNSS/IMU导航鲁棒性方面的优势。
原文摘要 · Abstract (English)
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.
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