Abstract
Unmanned helicopter has unique flight performance and plays an important role in the field of military and civil. Strong atmospheric disturbance environment is the comprehensive wind which has the effect on the body of the unmanned helicopter. Relying on the key project of NSFC (61533008), the research of the navigation interference and the state estimation method of the unmanned helicopter caused by strong airflow are focused in this paper. In view of the fact that only the inertial navigation system is difficult to meet the reliability and accuracy requirements of the unmanned helicopter navigation system in the strong airflow environment, a multi-sensor integrated navigation method based on the frame of factor graph is proposed in this paper. The performance of the integrated navigation system of the unmanned helicopter in the strong airflow environment can be effectively improved by using the factor graph filtering method. At the end of the paper, the simulation platform is built, and the algorithm proposed in the article is experimented by the simulation system. Then we analyzed the result and got the conclusion. The results show that the algorithm is helpful to solve the problem of navigation system fault caused by error increasing of navigation sensor of the unmanned aerial helicopter in the strong airflow interference environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Merz, T., Chapman, S.: Autonomous unmanned helicopter system for remote sensing missions in unknown environments. Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 38(9), 14–16 (2011)
Zhao, W., Meng, W.: Factor graph based multi-source data fusion for wireless localization. In: 2016 IEEE Wireless Communications and Networking Conference, pp. 1–6. IEEE, Doha (2016)
Pfeifer, T., Weissig, P., Lange, S., Protzel, P.: Robust factor graph optimization—a comparison for sensor fusion applications. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4. IEEE, Berlin (2011)
Indelman, V., Williams, S., Kaess, M.: Information fusion in navigation systems via factor graph based incremental smoothing. Robot. Auton. Syst. 61(8), 721–738 (2013)
Lupton, T., Sukkarieh, S.: Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Trans. Rob. 28(1), 61–76 (2012)
Forster, C., Carlone, L., Dellaert, F.: IMU preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. Robot. Sci. Syst. (2015)
Zeng, Q., Chen, W., Liu, J.: An improved multi-sensor fusion navigation algorithm based on the factor graph. Sensors 17(3), 641–657(2017)
Ji, H., Chen, R., Li, P.: Distributed atmospheric turbulence model for helicopter flight simulation and handling-quality analysis. J. Aircr. 54(1), 1–9 (2017)
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61533008, 61374115, 61603181),the Fundamental Research Funds for the Central Universities (No. NS2018021) & the Priority Academic Program Development of Jiangsu Higher Education Institutions, Jiangsu Key Laboratory of Internet of Things and Control Technologies (NUAA) & Key Laboratory of Navigation, Guidance and Health-Management Technologies of Advanced Aerocraft (NUAA).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, Kc., Zeng, Qh., Liu, Jy., Zhou, Yj. (2022). Multi-Source Navigation Data Fusion Based on Factor Graph for Unmanned Helicopter Under Atmospheric Disturbance. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_184
Download citation
DOI: https://doi.org/10.1007/978-981-15-8155-7_184
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8154-0
Online ISBN: 978-981-15-8155-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)