自适应因子图基于紧密耦合GNSS/IMU融合的稳健定位
Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin
- School of Technology and Innovations, University of Vaasa（瓦萨大学技术与创新学院）
- Information Technology, Åbo Akademi University（阿博阿卡迪米大学信息技术系）
- Faculty of Information Technology and Communication Sciences, Tampere University（塔尔库大学信息科技与通信科学学院）
- Finnish Geospatial Research Institute（芬兰地理空间研究 institute）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种基于自适应因子图的GNSS/IMU融合方法，利用Barron损失提升鲁棒性，有效减少定位误差。
AI中文摘要：
在GNSS挑战性环境中实现可靠的定位仍然是导航系统的关键挑战。紧密耦合的GNSS/IMU融合提高了鲁棒性，但仍易受非高斯噪声和离群值影响。我们提出了一种稳健且自适应的因子图融合框架，该框架直接整合GNSS伪距测量与IMU预积分因子，并引入Barron损失，一种通用的鲁棒损失函数，通过单个可调参数统一了多种m-估计量。通过自适应降低不可靠的GNSS测量值权重，我们的方法提高了定位的鲁棒性。该方法在扩展的GTSAM框架中实现，并在UrbanNav数据集上评估。所提出的方法将定位误差减少到标准FGO的41%，并在城市峡谷环境中比扩展卡尔曼滤波器（EKF）基线实现了更大的改进。这些结果突显了Barron损失在增强GNSS/IMU导航鲁棒性方面的优势，特别是在城市和信号受损环境中。
英文摘要：
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.
