Abstract
The autonomous navigation system is of vital importance and great urgency for unmanned ground vehicles (UGVs) in GNSS-challenged/denied environments. This paper aims to develop a robust IMU/vision/geomagnetic integrated navigation system, which can provide position and attitude estimation for UGVs during long endurance. The core idea of this paper is to integrate the navigation information estimated by continuous vision, IMU mechanization, and Geomagnetic measurement based on a federated Kalman filter (FKF). Considering the instability of the continuous vision system, the global position generated by its matching with the prior map is introduced to the IMU/vision subfilter. Together with the local attitude and position, the measurement of the subfilter is formed. In order to improve the robustness of the global attitude, the north angle and the geomagnetic increment are utilized together to form an IMU/geomagnetic subfilter. Then, the FKF algorithm is designed to integrate the two subfilters. Experiments in the urban roads are carried out and the results demonstrate the effectiveness of the proposed system. The statistical results of dozens of experiments show that the attitude error of the proposed system is 0.384\(^{\circ }\) (2d RMS, 95%) and the positioning error is 3.77 m (2d RMS, 95%).
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The data supporting the findings of this study are available within the article. The raw data of SINS, MEMS, vision, and GNSS can be obtained through email with the permission of the authors.
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Acknowledgements
The authors would like to thank the anonymous reviewers for their helpful comments.
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This work was funded by the National Defense Fundamental Scientific Research of Central Military Commission (CMC), Grant number 2019-JCJQ-2D-078.
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Conceptualization, ZF and CY; methodology, CM; software, ZF, BL, and CM; validation, QZ, and PY; formal analysis, CY and CM; investigation, ZF and QZ; data curation, CM, BL, and PY; writing-original draft preparation, ZF.
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Appendix 1: Matrix in the mechanization of SINS
Appendix 1: Matrix in the mechanization of SINS
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Yang, C., Fan, Z., Zhu, Q. et al. Robust navigation system for UGV based on the IMU/vision/geomagnetic fusion. Int J Intell Robot Appl 7, 321–334 (2023). https://doi.org/10.1007/s41315-023-00277-z
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DOI: https://doi.org/10.1007/s41315-023-00277-z