Email this article Positioning of large objects by computer vision methods
- Authors: Lopatina V.V.1
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Affiliations:
- Federal Research Center “Computer Science and Control,” Russian Academy of Sciences
- Issue: No 5 (2024)
- Pages: 138-148
- Section: ARTIFICIAL INTELLIGENCE
- URL: https://ter-arkhiv.ru/0002-3388/article/view/681848
- DOI: https://doi.org/10.31857/S0002338824050095
- EDN: https://elibrary.ru/TDYUQK
- ID: 681848
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Abstract
The article presents the structure of a measuring complex that allows high-precision measurements of the position of objects relative to a stationary base using computer vision methods based on optical meters data. The principle of operation of the measuring complex is described. The procedure for using and the features of adjusting the elements is determined. The operation of the measuring complex is illustrated by an example from the maritime transport industry i.e. by the solution of the problem of monitoring the position of an autonomous marine large-tonnage vessel relative to the berth when performing loading and unloading operations and mooring operations. Methods of using the measuring complex in road, air and rail transport are also described.
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About the authors
V. V. Lopatina
Federal Research Center “Computer Science and Control,” Russian Academy of Sciences
Author for correspondence.
Email: int00h@mail.ru
Russian Federation, Moscow
References
- Poujouly S., Journet B. A Twofold Modulation Frequency Laser Range Finder // J. Optics A: Pure and Applied Optics. 2002. № 4. P. 356–363.
- Zheng X.Y., Zhao C., Zhang H.Y., Zheng Z., Yang H.Z. Coherent Dual-frequency Lidar System Design for Distance and Speed Measurements // Intern. Conf. on Optical Instruments and Technology: Advanced Laser Technology and Applications. International Society for Optics and Photonics. Beijing, China, 2018. V. 10619.
- Jia F.X., Yu J.Y., Ding Z.L., Yuan F. Research on Real-time Laser Range Finding System // Applied Mechanics and Materials. 2013. V. 347.
- Beraldin J.A., Steenaart W. Overflow Analysis of a Fixed-Point Implementation of the Goertzel Algorithm // IEEE Transactions on Circuits and Systems. 1989. V. 36. № 2. P. 322–324.
- Finlayson D.M., Sinclair B. Advances in Lasers and Applications // Boca Raton. Florida, USA. CRC Press, 1998. P. 346.
- Lopatina V.V. Method of Fragment Based Tracking of Displacement of a Large Areal Object in Images // J. Phys.: Conf. Ser. 2021. V. 2061. P. 012113. https://doi.org/10.1088/1742–6596/2061/1/012113.
- Kalman R.E. A New Approach to Linear Filtering and Prediction Problems // J. Basic Eng. 1960. V. 82(1). P. 35–45.
- Welch G., Bishop G. An Introduction to the Kalman Filter // Technical Report. University of North Carolina at Chapel Hill, USA. 1995. V. 95–041.
- Grewal M.S., Andrews A.P. Kalman Filtering: Theory and Practice Using MATLAB. N. Y., USA: John Wiley and Sons, 2001. https://doi.org/10.1002/9780470377819.
- Линник Ю.В. Метод наименьших квадратов и основы математико-статистической теории обработки наблюдений. Изд. 2-е, исправ. и доп. М.: Физматгиз, 1962. 349 c.
- Митин И.В., Русаков В.С. Анализ и обработка экспериментальных данных. Учебно-методическое пособие для студентов младших курсов. М.: Изд-во НЭВЦ ФИПТ, 1998. 48 c. ISBN5–8279–0022–2.
- Богданов А.Ф., Иванов И.А., Терехов П.М. Восстановление профиля поверхности катания колесных пар без выкатки // Бюллетень результатов научных исследований. С.- Петербург: Изд. Петербургского государственного университета путей сообщения. 2014. № 1. С. 58–68.
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