面向基于因子图优化的GNSS-RTK/INS/里程计融合的两阶段GNSS异常值检测
Two stage GNSS outlier detection for factor graph optimization based GNSS-RTK/INS/odometer fusion
AI总结：
针对复杂环境下GNSS伪距异常值导致定位精度下降的问题，提出基于多普勒检测与IMU/里程计约束的两阶段异常值检测方法，应用于FGO框架下的GNSS-RTK/INS/里程计紧耦合系统，有效提升了定位鲁棒性与精度。
AI中文摘要：
在复杂环境中实现可靠的GNSS定位仍是一项关键挑战，这主要源于非视距（NLOS）传播、多径效应以及频繁的信号遮挡。这些影响极易在原始伪距测量值中引入大量异常值，从而显著降低全球导航卫星系统（GNSS）实时动态（RTK）定位的性能，并限制紧耦合GNSS组合导航系统的有效性。为解决这一问题，我们提出了一种两阶段异常值检测方法，并将其应用于基于因子图优化（FGO）的GNSS-RTK、惯性导航系统（INS）与里程计紧耦合组合系统中。在第一阶段，我们采用多普勒测量值以纯GNSS的方式检测伪距异常值——与伪距相比，多普勒对多径和NLOS效应的敏感度更低，因此可作为检测突变不一致性的更稳定参考。在第二阶段，我们利用预积分惯性测量单元（IMU）和里程计约束生成预测的双差伪距测量值，从而对剩余异常值进行更精细的识别与剔除。通过结合这两个互补的阶段，该系统对伪距粗差和卫星测量质量下降的鲁棒性均得到提升。实验结果表明，与代表性基线方法相比，该两阶段检测框架显著降低了伪距异常值的影响，并提高了定位精度与一致性。在深城市峡谷测试中，该异常值抑制方法将GNSS-RTK/INS/里程计融合的均方根误差（RMSE）从0.52米降至0.30米，提升幅度达42.3%。
英文摘要：
Reliable GNSS positioning in complex environments remains a critical challenge due to non-line-of-sight (NLOS) propagation, multipath effects, and frequent signal blockages. These effects can easily introduce large outliers into the raw pseudo-range measurements, which significantly degrade the performance of global navigation satellite system (GNSS) real-time kinematic (RTK) positioning and limit the effectiveness of tightly coupled GNSS-based integrated navigation system. To address this issue, we propose a two-stage outlier detection method and apply the method in a tightly coupled GNSS-RTK, inertial navigation system (INS), and odometer integration based on factor graph optimization (FGO). In the first stage, Doppler measurements are employed to detect pseudo-range outliers in a GNSS-only manner, since Doppler is less sensitive to multipath and NLOS effects compared with pseudo-range, making it a more stable reference for detecting sudden inconsistencies. In the second stage, pre-integrated inertial measurement units (IMU) and odometer constraints are used to generate predicted double-difference pseudo-range measurements, which enable a more refined identification and rejection of remaining outliers. By combining these two complementary stages, the system achieves improved robustness against both gross pseudo-range errors and degraded satellite measuring quality. The experimental results demonstrate that the two-stage detection framework significantly reduces the impact of pseudo-range outliers, and leads to improved positioning accuracy and consistency compared with representative baseline approaches. In the deep urban canyon test, the outlier mitigation method has limits the RMSE of GNSS-RTK/INS/odometer fusion from 0.52 m to 0.30 m, with 42.3% improvement.
