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Performance Comparison of GNSS/INS Integrations Based on EKF and Factor Graph Optimization

机译:基于EKF和因子图优化的GNSS / INS集成的性能比较

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Integration of global navigation satellite system (GNSS) and inertial navigation system (INS) is extensively studied in the past decades. Conventionally, the two most common integration solutions are the loosely and the tightly coupled integrations using extended Kalman filter (EKF). Recently, the factor graph technique is adopted to integrate the GNSS/INS and improved performance is obtained compared with the EKF-based GNSS/INS integration. However, only simulated data are tested to show the effectiveness of factor graph-based method in the existing work. Moreover, the reason that why the factor graph-based integration obtains better performance is not presented in the existing reference. Therefore, this paper proposes to compare the performance of EKF, and the factor graph-based GNSS/INS integrations. Both loosely and tightly coupled integrations are comprehensively discussed. We test the four different GNSS/INS integration methods in typical urban scenario in Hong Kong. The performances of the four solutions are compared. The conclusion shows that the factor graph-based tightly coupled GNSS/INS integration obtains the best performance among the four methods. The detailed analysis of the reasons for the improvement caused by factor graph is also given in the paper from the angles of re-linearization and iteration.
机译:在过去的几十年中,全球导航卫星系统(GNSS)和惯性导航系统(INS)的集成在过去的几十年中进行了广泛的研究。传统上,使用扩展卡尔曼滤波器(EKF)是一种松散和紧密的耦合集成的两个最常见的集成解决方案。最近,采用因子图技术集成GNSS / INS,与基于EKF的GNSS / INS集成相比,获得了改进的性能。然而,仅测试模拟数据以显示基于因子图的方法在现有工作中的有效性。此外,在现有参考中没有提出基于因子图的集成获得更好的性能的原因。因此,本文建议比较EKF的性能,以及基于因子图的GNSS / INS集成。综合讨论了松散和紧密的耦合集成。我们在香港的典型城市情景中测试四种不同的GNSS / INS集成方法。比较了四种解决方案的性能。结论表明,基于因子图的紧密耦合的GNSS / INS集成在四种方法中获得了最佳性能。从重新线性化和迭代的角度,还给出了因子图引起的改进原因的详细分析。

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