An EKF-Based Satellite-Visual-Inertial Integrated Navigation Algorithm for Agricultural Machinery
DOI:
https://doi.org/10.54097/sq2ygq73Keywords:
Extended Kalman Filter, Agricultural Machinery, Satellite-Visual-Inertial Integrated Navigation AlgorithmAbstract
In order to overcome the limitations of relying on a single sensor in a complex agricultural environment, this paper studies the integration of navigation parameters across satellite, visual and inertial platforms. The different characteristics of these sensors are analyzed in detail to establish the state equation. Based on these mathematical models, an extended Kalman filter (EKF) is designed. Through state estimation and measurement update of different data sets, the efficient synthesis of Global Navigation Satellite System (GNSS), visual recognition module and Inertial Navigation System (INS) information is realized. In addition, MATLAB is used for comparative simulation. The results show that the proposed multi-sensor integrated framework has higher accuracy and robustness than single GNSS and dual GNSS / INS systems. Among them, the east position error is constrained between-0.42 ~ 0.37 m, and the north position error is constrained between-0.22 ~ 0.26 m.
Downloads
References
[1] Van Dinh, N., & Kim, G. W. (2020). Multi-sensor fusion towards VINS: A concise tutorial, survey, framework and challenges. In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 459–462). IEEE. https://doi.org/10.1109/BigComp48618.2020.00087.
[2] Wang, H., Li, D., Wu, C., & Yu, X. (2019). Depth perception of moving objects via structured light sensor with unstructured grid. Results in Physics, 13, 102163. https://doi.org/ 10. 1016/j.rinp.2019.102163.
[3] De Silva, V., Roche, J., & Kondoz, A. (2017). Fusion of LiDAR and camera sensor data for environment sensing in driverless vehicles. arXiv preprint arXiv:1707.07942.
[4] Delmerico, J., & Scaramuzza, D. (2018). A benchmark comparison of monocular visual-inertial odometry algorithms for flying robots. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2502–2509). IEEE. https://doi.org/10.1109/ICRA.2018.8460552.
[5] Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. https://doi.org/10.1109/TRO.2016.2624754.
[6] Sun, R., et al. (2020). Robust IMU/GPS/VO integration for vehicle navigation in GNSS degraded urban areas. IEEE Sensors Journal, 20(17), 10110–10122. https://doi.org/1 0. 1109/JSEN.2020.2992212.
[7] Reitbauer, E., & Schmied, C. (2021). Bridging GNSS outages with IMU and odometry: A case study for agricultural vehicles. Sensors, 21(13), 4467. https://doi.org/10.3390/s21134467.
[8] Zhang, G., & Hsu, L. T. (2018). Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system. Aerospace Science and Technology, 80, 368–380. https://doi.org/10.1016/j.ast.2018.07.029.
[9] Jing, Y., Li, Q., Ye, W., et al. (2023). Development of a GNSS/INS-based automatic navigation land levelling system. Computers and Electronics in Agriculture, 213, 108187. https://doi.org/10.1016/j.compag.2023.108187.
[10] Shalal, N., Low, T., McCarthy, C., et al. (2013). A review of autonomous navigation systems in agricultural environments. In SEAg 2013: Innovative Agricultural Technologies for a Sustainable Future.
[11] Demyanov, V. (2022). GNSS overview. In Space Weather Impact on GNSS Performance (pp. 5–87). Springer International Publishing. https://doi.org/10.1007/978-3-030-94502-6_2.
[12] Cheng, F., Feng, L., & Zhang, Y. (2004). The application of the Beidou Navigation System in marine hydrology and weather monitoring systems. Journal of Ocean Technology, 23(3), 70–73.
[13] Wang, J. B., Fang, S. S., Zhang, J. J., et al. (2017). Experimental analysis of BDS/GPS network differential positioning. Science of Surveying and Mapping, 42(4), 157–161.
[14] Li, Z., Yuan, Y., Fan, L., et al. (2013). Determination of the differential code bias for current BDS satellites. IEEE Transactions on Geoscience and Remote Sensing, 52(7), 3968–3979. https://doi.org/10.1109/TGRS.2013.2251916.
[15] Srinivas, P., & Kumar, A. (2017). Overview of architecture for GPS-INS integration. In 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE) (pp. 433–438). IEEE. https://doi.org/10.1109/RDCAPE.2017.8276254.
[16] Ragozin, D. L. (1974). Rotation invariant measure algebras on Euclidean space. Indiana University Mathematics Journal, 23(12), 1139–1154. https://doi.org/10.1512/ iumj. 1974. 23. 121139.
[17] Henderson, D. M. (1977). Euler angles, quaternions, and transformation matrices for space shuttle analysis [Technical report].
[18] Zhao, X. Y., Wen, L. S., & Mei, C. L. (2012). Second-order kinematic singularity of attitude expressed by Eulerian angles. Science Technology and Engineering, 20(3), 634–637.
[19] Goldman, R. (2011). Understanding quaternions. Graphical Models, 73(2), 21–49. https://doi.org/ 10.1016/j. gmod. 2010. 12. 004.
[20] Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter [Technical report].
[21] Kim, Y., & Bang, H. (2018). Introduction to Kalman filter and its applications. In Introduction and Implementations of the Kalman Filter (Vol. 1, pp. 1–16).
[22] Ribeiro, M. I. (2004). Kalman and extended Kalman filters: Concept, derivation and properties. Institute for Systems and Robotics, 43(46), 3736–3741.
[23] Barrau, A. (2015). Non-linear state error based extended Kalman filters with applications to navigation [Doctoral dissertation, Mines Paristech].
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Frontiers in Computing and Intelligent Systems
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
