Abstract
GNSS/INS tightly coupled system has become the highlight of integrated navigation system because of its proper computational complexity and superior navigation performance. However, frequent short-time loss of lock of GNSS signal has impacted on the positioning accuracy and robustness of the tightly coupled GNSS/INS integration greatly in the city canyons and other complex environment. The typical tightly coupled integration algorithm, such as Extended Kalman Filter, will be often divergent in the case of measurement outlier or fault. In this paper, the joint weight matrix of the internal parameters of GNSS receiver is proposed. The pseudo-range and pseudo-range rate measurement covariance matrixes are adjusted by the joint weight matrix in real time. The improved factor graph algorithm proposed in this paper has high navigation accuracy and system stability when the GNSS signal is outlier and frequent short-time loss of lock. Simulation and experiment results show that the improved factor graph algorithm has low computational complexity and higher stability than classical Extended Kalmam Filter algorithm and factor graph algorithm, and is more suitable in the actual engineering fields.
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References
Srinivas, P., Kumar, A.: Overview of architecture for GPS-INS integration. In: 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), Noida, pp. 433-438 (2017) https://doi.org/10.1109/RDCAPE.2017.8358310
Liu, Y., Fan, X., Lv, C., et al.: An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles. Mech. Syst. Sign. Process. 100, 605–616 (2017)
Falco, G., Pini, M., Marucco, G.: Loose and Tight GNSS/INS integrations: comparison of performance assessed in real urban scenarios. Sensors 17(2), 255 (2017)
Yan, Z., Chen, X., Tang, X.: A novel linear model based on code approximation for GNSS/INS ultra-tight integration system. Sensors 20(11), 3192 (2020)
Zhang, G., Wen, W.S, Hsu, T.L.: Correcting GNSS NLOS by 3D LiDAR and Building Height. In: ION GNSS+2018, Miami, Florida, USA (2018)
Wada, Y., Hsu, L.T., Gu, Y., Kamijo, S.: Optimization of 3D building models by GPS measurements. GPS Solutions 21(1), 65–78 (2017)
Wan, G., et al.: Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, pp. 4670–4677 (2018)
Wen, W.S., Bai, X.W., Chiu, Y., Hsu, L.T.: Tightly coupled GNSS/INS integration vis factor graph and aided by fish-eye camera. IEEE Trans. Veh. Tech. 68(11), 10651–10662 (2019)
Indelman, V., Williams, S., Kaess, M., et al.: Information fusion in navigation systems via factor graph based incremental smoothing. Robot. Auton. Syst. 61(8), 721–738 (2013)
Dai, H., Bian, H., Ma, H., et al.: Application of robust incremental smoothing algorithm based on factor graph in integrated navigation of unmanned surface vehicle. J. Chin. Inertial Technol. 26(6), 778–786 (2018)
Bao, S., Lai, J., Chen, Z., et al.: Aerodynamic model/INS/GPS failure-tolerant navigation method for multirotor UAVs based on federated Kalman Filter. In: 2017 Chinese Automation Congress (CAC) (2017)
Ning, Y., Wang, J., Han, H., et al.: An Optimal radial basis function neural network enhanced adaptive robust Kalman filter for GNSS/INS integrated systems in complex urban areas. Sensors 18(9), 581–587 (2018)
Dellaert, F.: Factor graphs and GTSAM: A hands-on introduction. Georgia Institute of Technology (2012)
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J.J., Dellaert, F.: iSAM2: Incremental smoothing and mapping using the Bayes tree. Int. J. Robot. Res. 31(2), 216–235 (2012)
Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 year, 1000 km: the oxford RobotCar dataset. Int. J. Robot. Res. 36(1), 3–15 (2016)
Li, W., Cui, X.W., Lu, M.Q.: A robust graph optimization of tightly coupled GNSS/INS integrated navigation system for urban vehicles. Tsinghua Sci. Technol. 23(6), 724–730 (2018)
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Liu, X., Yuan, Y., Huang, J., Xiao, Y., Li, X. (2021). A New Algorithm of Tightly-Coupled GNSS/INS Integrated Navigation Based on Factor Graph. In: Yang, C., Xie, J. (eds) China Satellite Navigation Conference (CSNC 2021) Proceedings. Lecture Notes in Electrical Engineering, vol 773. Springer, Singapore. https://doi.org/10.1007/978-981-16-3142-9_56
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DOI: https://doi.org/10.1007/978-981-16-3142-9_56
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