The integrated navigation of global satellite navigation system (GNSS) and inertial measurement unit (IMU) faces the problems of satellite signal limitation and IMU error accumulation in complex environments such as forests and long tunnels. Adding visual sensors is a good solution. In the majority of existing researches, non-Gaussian ambient noise is mostly regarded as Gaussian noise to simplify the processing, how to deal with non-Gaussian ambient noise remains a burning issue. In this paper, a minimum error entropy (MEE) based iterative extended Kalman filtering (IEKF) method for GPS/IMU/Visual-integrated navigation is developed. First, IEKF is employed to process GPS abnormal data and gradually approach the real state through an iterative optimization. Second, the MEE criterion is combined to enhance the robustness of the system to non-Gaussian noise, and the fixed-point iteration method is used to calculate the state estimation to improve the positioning accuracy of the system. Finally, the Canadian and Katwijk navigation data sets are utilized to validate the effectiveness of the proposed method. The results show that compared with the traditional EKF-based method, the proposed method can reduce the longitude error by 5.9% and 29.2%, and the latitude error by 19.3% and 56.6%, respectively, in GPS abnormal and non-Gaussian noise environments, and the accuracy and robustness of the navigation system are significantly improved.
ChenBDangLGuY, et al. (2019) Minimum error entropy Kalman filter. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(9): 5819–5829.
2.
ChenWLiZChenZ, et al. (2023a) Multiple similarity measure-based maximum correntropy criterion Kalman filter with adaptive kernel width for GPS/INS integration navigation. Measurement222: 113666.
3.
ChenYXuBWangB, et al. (2023b) GNSS reconstrainted visual–inertial odometry system using factor graphs. IEEE Geoscience and Remote Sensing Letters20: 1–5.
4.
CongLYueSQinH, et al. (2020) Implementation of a MEMS-based GNSS/INS integrated scheme using supported vector machine for land vehicle navigation. IEEE Sensors Journal20(23): 14423–14435.
5.
DaiHBianHWangR, et al. (2020) An INS/GNSS integrated navigation in GNSS denied environment using recurrent neural network. Defence Technology16(2): 334–340.
6.
DawsonERashedMAAbdelfatahW, et al. (2022) Radar-based multisensor fusion for uninterrupted reliable positioning in GNSS-denied environments. IEEE Transactions on Intelligent Transportation Systems23(12): 23384–23398.
7.
EckenhoffKGenevaPHuangG (2019) Sensor-failure-resilient multi-IMU visual-inertial navigation. In:2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May, pp. 3542–3548. New York: IEEE.
8.
EckenhoffKGenevaPHuangG (2021) MIMC-VINS: A versatile and resilient multi-IMU multi-camera visual-inertial navigation system. IEEE Transactions on Robotics37(5): 1360–1380.
9.
FanXWangGHanJ, et al. (2022) A background-impulse Kalman filter with non-Gaussian measurement noises. IEEE Transactions on Systems, Man, and Cybernetics: Systems53(4): 2434–2443.
10.
FuZLiuYSiP, et al. (2023a) Adaptive fusion multi-IMU confidence level location algorithm in the absence of stars. IEEE Sensors Journal23(16): 18644–18655.
11.
FuZLiuYSiP, et al. (2023b) A multisensor high-precision location method in urban environment. IEEE Systems Journal17(4): 6611–6622.
12.
GaoGGaoBGaoS, et al. (2022) A hypothesis test-constrained robust Kalman filter for INS/GNSS integration with abnormal measurement. IEEE Transactions on Vehicular Technology72(2): 1662–1673.
13.
HanCWuWLuoX, et al. (2023) Visual navigation and obstacle avoidance control for agricultural robots via LiDAR and camera. Remote Sensing15(22): 5402.
14.
HewittRABoukasEAzkarateM, et al. (2018) The Katwijk beach planetary rover dataset. The International Journal of Robotics Research37(1): 3–12.
15.
JiangCZhangSLiH, et al. (2021) Performance evaluation of the filters with adaptive factor and fading factor for GNSS/INS integrated systems. GPS Solutions25: 1–11.
16.
KimJChoiYParkM, et al. (2020) Multi-sensor-based detection and tracking of moving objects for relative position estimation in autonomous driving conditions. The Journal of Supercomputing76: 8225–8247.
17.
LamarreOLimoyoOMarićF, et al. (2020) The Canadian planetary emulation terrain energy-aware rover navigation dataset. The International Journal of Robotics Research39(6): 641–650.
18.
LeeTNCancianiAJ (2020) MagSLAM: Aerial simultaneous localization and mapping using earth’s magnetic anomaly field. Navigation67(1): 95–107.
19.
LiMJingZZhuH, et al. (2022) Multi-sensor measurement fusion based on minimum mixture error entropy with non-Gaussian measurement noise. Digital Signal Processing123: 103377.
20.
LiNGuanLGaoY, et al. (2020) Indoor and outdoor low-cost seamless integrated navigation system based on the integration of INS/GNSS/LIDAR system. Remote Sensing12(19): 3271.
21.
LiSLiXWangH, et al. (2023a) Multi-GNSS PPP/INS/Vision/LiDAR tightly integrated system for precise navigation in urban environments. Information Fusion90: 218–232.
22.
LiXXiaCLiS, et al. (2023b) A filter-based integration of GNSS, INS, and stereo vision in tight mode with optimal smoothing. IEEE Sensors Journal23(19): 23238–23254.
23.
LiZChenWSunY, et al. (2023c) An improved multiple-outlier robust filter based on maximum correntropy criterion for integrated navigation. IEEE Sensors Journal23(15): 17451–17461.
NiuXTangHZhangT, et al. (2022) IC-GVINS: A robust, real-time, INS-centric GNSS-visual-inertial navigation system. IEEE Robotics and Automation Letters8(1): 216–223.
26.
RenJZiJYangH, et al. (2021) Performance analysis and architectures for a MEMS-SINS/GPS ultratight integration system. Mathematical Problems in Engineering2021(1): 8892205.
27.
WangN (2022) Satellite/inertial navigation integrated navigation method based on improved Kalman filtering algorithm. Mobile Information Systems2022(1): 4627111.
28.
WuJJiangJZhangC, et al. (2023) A novel optimal robust adaptive scheme for accurate GNSS RTK/INS tightly coupled integration in urban environments. Remote Sensing15(15): 3725.
29.
XiongHBianRLiY, et al. (2020) Fault-tolerant GNSS/SINS/DVL/ CNS integrated navigation and positioning mechanism based on adaptive information sharing factors. IEEE Systems Journal14(3): 3744–3754.
30.
XuBWangXZhangJ, et al. (2021a) Maximum correntropy delay Kalman filter for SINS/USBL integrated navigation. ISA Transactions117: 274–287.
31.
XuCLiuZLiZ (2021b) Robust visual-inertial navigation system for low precision sensors under indoor and outdoor environments. Remote Sensing13(4): 772.
32.
YangBXueLFanH, et al. (2021) SINS/odometer/Doppler radar high-precision integrated navigation method for land vehicle. IEEE Sensors Journal21(13): 15090–15100.
33.
YazdkhastiSSabzevariDSasiadekJZ (2023) Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties. Transactions of the Institute of Measurement and Control45(8): 1430–1442.
34.
YinZYangJMaY, et al. (2023) A robust adaptive extended Kalman filter based on an improved measurement noise covariance matrix for the monitoring and isolation of abnormal disturbances in GNSS/INS vehicle navigation. Remote Sensing15(17): 4125.
35.
YueZLianBTangC, et al. (2020) A novel adaptive federated filter for GNSS/INS/VO integrated navigation system. Measurement Science and Technology31(8): 085102.
36.
ZhongYChenXZhouY, et al. (2023) Adaptive particle filtering with variational Bayesian and its application for INS/GPS integrated navigation. IEEE Sensors Journal23(17): 19757–19770.
37.
ZhuBLiDLiZ, et al. (2021) Robust adaptive Kalman filter for strapdown inertial navigation system dynamic alignment. IET Radar, Sonar & Navigation15(12): 1583–1593.
