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High-accuracy multi-cameras calibration in constrained assembly spaces: application to wing-fuselage docking of large aircraft

Published online by Cambridge University Press:  17 December 2025

X. Tian ,
Y. Zhu Open the ORCID record for Y. Zhu [Opens in a new window] ,
D. Li ,
W. Jiang Open the ORCID record for W. Jiang [Opens in a new window]  and
B. Hou
X. Tian
Affiliation:
School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University, Nanchang, China
Y. Zhu*
Affiliation:
School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University, Nanchang, China
D. Li
Affiliation:
School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University, Nanchang, China
W. Jiang
Affiliation:
School of Aeronautical Manufacturing and Mechanical Engineering, Nanchang Hangkong University, Nanchang, China
B. Hou
Affiliation:
AVIC Changhe Aircraft Industry (Group) Corporation Ltd, Jingdezhen, China
*
Corresponding author: Y. Zhu; Email: zhuyongguo_2003@163.com
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Abstract

In the process of utilising machine vision-assisted large aircraft component docking assembly, due to the occlusion induced by process equipment such as assembly tooling, the features on the calibration board cannot be extracted by each camera at the same time, resulting in calibration difficulties or calibration failure. This paper aims to propose a stereo calibration method for multi-cameras in large aircraft component assembly to improve calibration accuracy. Firstly, the sub-pixel edge extraction method based on Canny-Zernike is proposed to accurately extract the circular edges and circle centres of the calibration board, and the Zernike moment model is improved. The circle centre sorting method based on the triangular markers is introduced to realise the sorting of circle centres on the calibration board. Secondly, the intrinsic and extrinsic parameter models of multi-cameras and the visual parameter models between cameras are constructed, and Zhang’s calibration method and indirect calibration method are integrated to solve the parameters. Subsequently, the distortion correction model is optimised by Levenberg-Marquardt. Finally, experiments are performed to test the proposed method. The results show that the proposed method, compared with uncalibration and Zhang’s calibration method, the proposed method achieves stereo calibration of the multi-cameras under complex working conditions, enhances the calibration accuracy and improves the quality of the large aircraft component docking assembly.

Keywords

aircraftdocking assemblylarge aircraft componentsmulti-camerasstereo calibration

Information

Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 25
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Zhang, H., Li, L., Xu, Y., et al. A vision measuring method for fork type wing-fuselage docking, Aeronaut. Manuf. Technol., 2017, pp 56–61. https://doi.org/10.16080/j.issn1671-833x.2017.21.056 Google Scholar
Zhu, Y., Zhang, W., Deng, Z., et al. Dynamic synthesis correction of deviation for aircraft wing-fuselage docking assembly based on laser tracker and machine vision, J. Mech. Eng., 2019, 55, pp 187–196. https://doi.org/10.3901/JME.2019.24.187 Google Scholar
Zha, Q.S., Zhu, Y.G. and Zhang, W.B. Visual and automatic wing-fuselage docking based on data fusion of heterogeneous measuring equipments, J. Chin. Inst. Eng., 2021, 44, pp 792–802. https://doi.org/10.1080/02533839.2021.1978324 CrossRefGoogle Scholar
Liu, X., Tian, J., Kuang, H., et al. A stereo calibration method of multi-cameras based on circular calibration board, Electronics, 2022, 11, p 627. https://doi.org/10.3390/electronics11040627 CrossRefGoogle Scholar
Sun, H., Lu, J. and Chang, Z. An efficient camera calibration and optimisation method based on orthogonal vanishing points, Imaging Sci. J., 2016, 64, pp 232–239. https://doi.org/10.1080/13682199.2016.1168143 CrossRefGoogle Scholar
Chen, H., Zhuang, J., Liu, B., et al. Camera calibration method based on circular array calibration board, Syst. Sci. Control Eng., 2023, 11, pp 1–12. https://doi.org/10.1080/21642583.2023.2233562 Google Scholar
Zhang, Z. Flexible camera calibration by viewing a plane from unknown orientations, Proceedings of the Seventh IEEE International Conference on Computer Vision, IEEE, Vol. 1, 1999, pp. 666–673. https://doi.org/10.1109/ICCV.1999.791289 CrossRefGoogle Scholar
Zhang, Z. A flexible new technique for camera calibration, IEEE Trans. Pattern. Anal. Mach. Intell., 2002, 22, pp 1330–1334. https://doi.org/10.1109/34.888718 CrossRefGoogle Scholar
Li, D., Wang, G., Liu, J., et al. Joint internal and external parameters calibration of optical compound eye based on random noise calibration pattern, J. Electron. Inf. Technol., 2024, 46, pp 2898–2907. https://doi.org/10.11999/JEIT230652 Google Scholar
Meng, X. and Hu, Z. A new easy camera calibration technique based on circular points, Pattern Recognit., 2023, 36, pp 1155–1164. https://doi.org/10.1016/S0031-3203(02)00225-X CrossRefGoogle Scholar
Zhang, Y., Zhou, F. and Deng, P. Camera calibration approach based on adaptive active target, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, SPIE, 2012, Vol. 8350, pp 318–324. https://doi.org/10.1117/12.922899 CrossRefGoogle Scholar
Jia, Z., Yang, J., Liu, W., et al. Improved camera calibration method based on perpendicularity compensation for binocular stereo vision measurement system, Opt Express, 2015, 23, pp 15205–15223. https://doi.org/10.1364/OE.23.015205 CrossRefGoogle ScholarPubMed
Chen, Z., Si, X., Wu, D., et al. A novel camera calibration method based on known rotations and translations, Comput. Vis. Image Underst., 2024, 243, pp 1–10. https://doi.org/10.1016/j.cviu.2024.103996 CrossRefGoogle Scholar
Dang, T., Hoffmann, C. and Stiller, C. Continuous stereo self-calibration by camera parameter tracking, IEEE Trans. Image. Process., 2009, 18, pp 1536–1550. https://doi.org/10.1109/TIP.2009.2017824 CrossRefGoogle ScholarPubMed
Jin, J. and Li, X. Efficient camera self-calibration method based on the absolute dual quadric, JOSA A, 2013, 30, pp 287–292. https://doi.org/10.1364/JOSAA.30.000287 CrossRefGoogle ScholarPubMed
Li, G., Huang, X. and Li, S. A novel circular points-based self-calibration method for a camera’s intrinsic parameters using RANSAC, Meas. Sci. Technol., 2019, 30, p 055005. https://doi.org/10.1088/1361-6501/ab09c0 CrossRefGoogle Scholar
Tian, X., Gao, Q., Luo, Q., et al. Trinocular camera self-calibration based on spatio-temporal multi-layer optimization, Measurement, 2023, 217, p 113003. https://doi.org/10.1016/j.measurement.2023.113003 CrossRefGoogle Scholar
Sekehravani, E.A., Babulak, E. and Masoodi, M. Implementing canny edge detection algorithm for noisy image, Bull. Electr. Eng. Inform., 2020, 9, pp 1404–1410. https://doi.org/10.11591/eei.v9i4.1837 CrossRefGoogle Scholar
Ge, X., Zhang, J., Zhou, Y., et al. Rough set neural network feature extraction and pattern recognition of shaft orbits based on the Zernike moment, Shock Vib., 2021, pp 1–8. https://doi.org/10.1155/2021/6680640 Google Scholar
Zixin, L., Jinyue, L., Yang, L., et al. A fast tool edge detection method based on Zernike moments algorithm’, IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2018, Vol. 439, No. 3, p 032106. https://doi.org/10.1088/1757-899X/439/3/032106 CrossRefGoogle Scholar
Xie, X., Ge, S., Xie, M., et al. An improved industrial sub-pixel edge detection algorithm based on coarse and precise location, J. Ambient. Intell. Hum. Comput., 2020, 11, pp 2061–2070. https://doi.org/10.1007/s12652-019-01232-2 CrossRefGoogle Scholar