提出首个基于因子图优化的多源导航系统完整性监测方法。
IM-GIV: an effective integrity monitoring scheme for tightly-coupled GNSS/INS/Vision integration based on factor graph optimization
- 通过残差线性化构建位置误差边界公式。
- 六类故障下完整性可用率达100%且能准确匹配误差。
- 适合高安全要求的自动驾驶与机器人定位场景。
基于因子图优化（FGO）的全球导航卫星系统/惯性导航系统/视觉融合导航近年来受到广泛关注。在安全关键应用中，该系统需具备完整性监测（IM）能力。然而，现有研究多基于卡尔曼滤波，难以适配FGO框架。本文首次设计并验证了基于FGO的GNSS/INS/视觉融合系统的定位误差边界公式，该公式由GNSS伪距、惯导预积分及视觉测量残差线性化得到。针对GNSS、惯导和视觉测量故障，给出了具体误差边界。实地实验表明，所提方法能准确拟合不同故障模式下的位置误差，且在正确及时排除故障后，六类故障模式下完整性可用率均为100%。
原文摘要 · Abstract (English)
Global Navigation Satellite System/Inertial Navigation System (GNSS/INS)/Vision integration based on factor graph optimization (FGO) has recently attracted extensive attention in navigation and robotics community. Integrity monitoring (IM) capability is required when FGO-based integrated navigation system is used for safety-critical applications. However, traditional researches on IM of integrated navigation system are mostly based on Kalman filter. It is urgent to develop effective IM scheme for FGO-based GNSS/INS/Vision integration. In this contribution, the position error bounding formula to ensure the integrity of the GNSS/INS/Vision integration based on FGO is designed and validated for the first time. It can be calculated by the linearized equations from the residuals of GNSS pseudo-range, IMU pre-integration and visual measurements. The specific position error bounding is given in the case of GNSS, INS and visual measurement faults. Field experiments were conducted to evaluate and validate the performance of the proposed position error bounding. Experimental results demonstrate that the proposed position error bounding for the GNSS/INS/Vision integration based on FGO can correctly fit the position error against different fault modes, and the availability of integrity in six fault modes is 100% after correct and timely fault exclusion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。