Calibration of the vision system sensors of the traction rolling stock
https://doi.org/10.46973/0201-727X_2023_2_20
Abstract
The paper considers an algorithm for automatic calibration of sensors of a vision system, which is used for all types of sensors with different physical principles. The vision system in this case acts as an element of the automatic train traffic control system and serves to detect obstacles in the way of the train. Separately, diagrams and descriptions of calibration are given for cameras in the visible range and thermal imagers with different focal lengths. For lidars, a description and a scheme for calculating external parameters are given, taking into account the transformation of a 3D point cloud into 2D pseudo-images to search for markers by distance, coordinate, and reflectivity with simultaneous filtering of pseudo-images by depth and reflectivity. The use of automatic calibration of vision system sensors during train preparation at the depot and when traveling at calibration stations will improve the accuracy of the measured train parameters, reduce response time to emergency situations, optimize energy costs, reduce payroll costs and reduce the impact of the human factor on train operation, and consequently improve traffic safety.
About the Authors
A. L. Okhotnikov
Research and Design Institute for Information Technology, Signalling and Telecommunications on Railway Transport (JSC “NIIAS”)
Russian Federation
Russian Federation
Okhotnikov Andrey Leonidovich - Deputy Head of the Information Technology Department – Head of the Strategic Development Department
A. V. Kostyukov
Rostov State Transport University (RSTU)
Russian Federation
Russian Federation
Kostyukov Alexander Vladimirovich - Candidate of Engineering Sciences, Associate Professor, Chair «Theoretical Foundations of Electrical Engineering»
References
1. Errors in measuring the distance to an obstacle by means of technical vision and braking path prediction in unmanned train traffic control systems / L. A. Baranov, P. F. Bestemyanov, E. P. Balakina, A. L. Okhotnikov // Mir Transporta (World of Transport and Transportation). – 2021. – Vol. 19, No. 6 (97). – P. 6–12. – DOI 10.30932/1992-3252-2021-19-6-1.
2. Rozenberg, E. N. Building an adaptive planning system for trains using digital data processing methods Data Science and Big data / E. N. Rozenberg, A. V. Ozerov, A. M. Olshansky // Intelligent transport systems : materials International Scientific and Practical Conference, Moscow, May 26, 2022. – Moscow : Russian University of Transport, 2022. – P. 285–289. – ISBN 978-5-00204-191-6.
3. Automatic calibration of multiple cameras and lidars for autonomous vehicles / Yu. B. Blokhinov, E. E. Andrienko, K. K. Kazakhmedov, B. V. Vishnyakov // Computer optics. – 2021. – Vol. 45, No. 3. – P. 382–393. – DOI 10.18287/2412-6179-CO-812.
4. Okhotnikov, A. L. Transport object as an element of the automatic control system / A. L. Okhotnikov // Science and Technology of Railways. – 2022. – Vol. 6, No. 3(23). – P. 45–52. – ISSN 2587-5752.
5. GOST R 8.879-2014. State system for ensuring the uniformity of measurements. Methods of calibration of measuring instruments. General requirements for content and presentation : National Standard of the Russian Federation: official publication: approved and put into effect by Order of the Federal Agency for Technical Regulation and Metrology dated November 20, 2014 No. 1701-st. – Moscow : Standartinform, 2015. – 6 p.
6. Zdorovtsov, A. A. Development and study of the calibration algorithm for a monocular speed camera / A. A. Zdorovtsov // Applied Mathematics: Modern Problems of Mathematics, Informatics and Modeling : Proceedings of the III AllRussian Scientific and Practical Conference of Young Scientists, Krasnodar, 21 – April 24, 2021. – Krasnodar : FSBI “Russian Energy Agency” of the Ministry of Energy of Russia Krasnodar CNTI – branch of the FGBU “REA” of the Ministry of Energy of Russia, 2021. – P. 150–155. – ISBN 978-5-91221-511-7.
7. Polyakov, A. R. Auto-calibration and synchronization of camera and MEMS-sensors / A. R. Polyakov, A. V. Kornilova, I. A. Kirilenko // Proceedings of the Institute for System Programming of the RAS. – 2018. – Vol. 30, No. 4. – P. 169–182. – DOI 10.15514/ISPRAS-2018-30(4)-11.
8. Senchenko, P. V. The problem of automatic calibration of the projector and depth sensor / P. V. Senchenko, D. A. Oznabikhin, V. F. Tarasenko // Tomsk State University Journal of Control and Computer Science. – 2016. – No. 2(35). – P. 71–77. – DOI 10.17223/19988605/35/8.
9. Kurokhtin, V. Yu. Determination of external calibration parameters of a vision system camera / V. Yu. Kurokhtin // Modern Technologies. System Analysis. Modeling. – 2017. – No. 1(53). – P. 132–136. – ISSN 1813-9108.
10. Kurokhtin, V. Yu. Determination of external calibration parameters of the vision system camera / V. Yu. Kurokhtin // Modern technologies. System Analysis. Modeling. – 2017. – No. 1(53). – P. 132–136. – ISSN 1813-9108.
11. Yakushev, D. A. Digital track model for unmanned train traffic control / D. A. Yakushev // Automation, Communication, Informatics. – 2021. – No. 4. – P. 35–39. – DOI 10.34649/AT.2021.4.4.005.
12. Terzakis, G. A Consistently Fast and Globally Optimal Solution to the Perspective-n-Point Problem / G. Terzakis, M. Lourakis // In Computer Vision – ECCV 2020 : Lecture Notes in Computer Science. Vol. 12346 / A. Vedaldi, H. Bischof, T. Brox, J. M. Frahm (eds). – Springer, Cham, 2020. – DOI 10.1007/978-3030-58452-8_28.
Review
For citations:
Okhotnikov A.L., Kostyukov A.V. Calibration of the vision system sensors of the traction rolling stock. Vestnik Rostovskogo Gosudarstvennogo Universiteta Putej Soobshcheniya. 2023;(2):20-29. (In Russ.) https://doi.org/10.46973/0201-727X_2023_2_20
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