Comparison of Extended Kalman Filter and Factor Graph Optimization for GNSS/INS Integrated Navigation System
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The integration of the global navigation satellite system (GNSS) and inertial navigation systems (INS) is extensively studied in the past decades for vehicular navigations, such as unmanned aerial vehicles (UAV) and autonomous driving vehicles (ADV). Conventionally, the two most common integration solutions are the loosely-coupled and the tightly-coupled integration using the extended Kalman filter (EKF). The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS which 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 accuracy and efficiency 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 first evaluates both loosely and tightly-coupled integrations using both EKF and FGO via the challenging dataset collected in the urban canyon of Hong Kong.The results show that the FGO-based tightly-coupled GNSS/INS integration obtains the best performance. 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 based on the validated dataset, by considering both the GNSS pseudorange error distribution and environmental conditions.
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