论文标题
现在是为GNSS/INS集成进行因子图优化的时间：FGO和EKF之间的比较
It is time for Factor Graph Optimization for GNSS/INS Integration: Comparison between FGO and EKF
论文作者
论文摘要
采用最近提出的因子图优化（FGO）来整合GNSS/INS吸引了很多关注，并改善了基于EKF的GNSS/INS集成的性能。但是，无法获得城市峡谷中这两个GNS/INS集成计划的全面比较。此外，基于FGO的GNSS/INS集成的性能在很大程度上取决于优化窗口的大小。有效地调整窗口大小仍然是一个悬而未决的问题。为了填补这一空白，本文通过在Urban Canyon中收集的具有挑战性的数据集评估了使用EKF和FGO均匀耦合的集成。本文还通过将基于FGO的估计量退化为EKF估算器，对FGO优势的结果进行了详细分析。更重要的是，我们通过考虑GNSS伪误差分布和环境条件来分析窗口大小对FGO性能的影响。
The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS attracted lots of attention and improved the performance over the existing EKF-based GNSS/INS integrations. However, a comprehensive comparison of those two GNSS/INS integration schemes in the urban canyon is not available. Moreover, the performance of the FGO-based GNSS/INS integration rely heavily on the size of the window of optimization. Effectively tuning the window size is still an open question. To fill this gap, this paper evaluates both loosely and tightly-coupled integrations using both EKF and FGO via the challenging dataset collected in the urban canyon. The detailed analysis of the results for the advantages of the FGO is also given in this paper by degenerating the FGO-based estimator to an EKF like estimator. More importantly, we analyze the effects of window size against the performance of FGO, by considering both the GNSS pseudorange error distribution and environmental conditions.
