Estimation of the geometrical measurement error at the stage of stereoscopic system design
Gorevoy A.V., Kolyuchkin V.Ya., Machikhin A.S.

 

Scientific and Technological Center of Unique Instrumentation, Russian Academy of Sciences, Moscow, Russia,
Bauman Moscow State Technical University, Moscow, Russia,
Moscow Power Engineering University, Moscow, Russia

 PDF

Abstract:
The article is dedicated to the error estimation method for stereoscopic systems measuring three-dimensional coordinates and geometric parameters of objects. This method is required for stereoscopic system design to optimize the parameters of the image acquisition system and the data processing algorithms. The technique should be suitable for different mathematical models of image acquisition systems and allow to access the measurement uncertainty with a known uncertainty in determining the coordinates of the corresponding points on the images and the uncertainty of the calibration parameters. We analyzed known methods by comparing their results with the Monte Carlo simulation for the pinhole and the ray tracing models. It is shown that the method using the unscented transformation provides better accuracy and versatility than the linearization method. Using the example of measuring the length of a segment, it is demonstrated that the use of a symmetric confidence interval constructed from the mean and variance can lead to an inaccurate estimation of the error in measuring geometric parameters. We propose a method for calculating confidence intervals based on a combination of unscented transformation and interval analysis and confirm its effectiveness by the computer simulation. The analysis is  applicable to the design of both passive stereoscopic devices and active triangulation systems as well as improving their software.

Keywords:
stereoscopic optical system, geometric parameter measurement, calibration, error estimation.

Citation:
Gorevoy AV, Kolyuchkin VYa, Machikihin AS. Estimation of the geometrical measurement error at the stage of stereoscopic system design. Computer Optics 2018; 42(6): 985-997. DOI: 10.18287/2412-6179-2018-42-6-985-997.

References:

  1. Wöhler C. 3D computer vision. Efficient methods and applications. 2nd ed. London: Springer-Verlag; 2013. ISBN: 978-1-4471-4149-5.
  2. Kim H, Lin CS, Song J, Chae H. Distance measurement using a single camera with a rotating mirror. Int J Control Autom Syst 2005; 3: 542-551.
  3. Chen Z, Wong K-Y, Matsushita Y, Zhu X. Depth from refraction using a transparent medium with unknown pose and refractive index. Int J Comput Vis 2013; 102(1-3): 3-17. DOI: 10.1007/s11263-012-0590-z.
  4. Cui X, Lim KB, Guo Q, Wang D. Accurate geometrical optic model for single-lens stereovision system using a prism. J Opt Soc Am A 2012; 29(9): 1828-1837. DOI: 10.1364/JOSAA.29.001828.
  5. Kee WL, Bai Y, Lim KB. Parameter error analysis of singlelens prism-based stereovision system. J Opt Soc Am A 2015; 32(3): 367-373. DOI: 10.1364/JOSAA.32.000367.
  6. Wu L, Zhu J, Xie H. Single-lens 3D digital image correlation system based on a bilateral telecentric lens and a biprism: validation and application. Appl Opt 2015; 54(26): 7842-7850. DOI: 10.1364/AO.54.007842.
  7. Gorevoy AV, Machikhin AS. Optimal calibration of a prism-based videoendoscopic system for precise 3D measurements. Computer Optics 2017; 41(4): 535-44. DOI: 10.18287/2412-6179-2017-41-4-535-544.
  8. Zhu J-G, Li YJ, Ye S-H. Design and calibration of a single-camera-based stereo vision sensor. Opt Eng 2006; 45(8): 083001. DOI: 10.1117/1.2336417.
  9. Zhou FQ, Wang YX, Peng B, Cui Y. A novel way of understanding for calibrating stereo vision sensor constructed by a single camera and mirrors. Measurement 2013; 46(3): 1147-1160. DOI: 10.1016/j.measurement.2012.10.031.
  10. Pan B, Wang Q. Single-camera microscopic stereo digital image correlation using a diffraction grating. Opt Express 2013; 21(21): 25056-25068. DOI: 10.1364/OE.21.025056.
  11. Sturm P, Ramalingam S, Tardif J-P, Gasparini S, Barreto J. Camera models and fundamental concepts used in geometric computer vision. Foundations and Trends in Computer Graphics and Vision 2011; 6(1-2): 1-183. DOI: 10.1561/0600000023.
  12. Gorevoy AV, Kolyuchkin VYa. Methods for estimation of coordinate measurement uncertainty of multichannel 3D imaging systems [In Russian]. Engineering Journal: Science and Innovation 2013; 9(21). Source: áhttp://engjournal.ru/catalog/pribor/optica/923.htmlñ. DOI: 10.18698/2308-6033-2013-9-923.
  13. Jiang C, Fu C-M, Ni B-Y, Han X. Interval arithmetic operations for uncertainty analysis with correlated interval variables. Acta Mechanica Sinica 2016; 32(4): 743-752. DOI: 10.1007/s10409-015-0525-3.
  14. Farenzena M, Busti A, Fusiello A, Benedetti A. Rigorous accuracy bounds for calibrated stereo reconstruction. Proc 17th International Conference on Pattern Recognition 2004; 4: 288-292. DOI: 10.1109/ICPR.2004.1333760.
  15. Telle B, Stasse O, Ueshiba T, Yokoi K, Tomita F. 3D boundaries partial representation of objects using interval analysis. Proc IEEE/RSJ International Conference on Intelligent Robots and Systems 2004; 4: 4013-4018. DOI: 10.1109/IROS.2004.1390042.
  16. Mustafa M, Stancu A, Guteirrez SP, Codres EA, Jaulin L. Rigid transformation using interval analysis for robot motion estimation. Proc 20th International Conference on Control Systems and Computer Science 2015: 24-31. DOI: 10.1109/CSCS.2015.98.
  17. Blostein SD, Huang TS. Error analysis in stereo determination of 3-D point positions. IEEE Trans Pattern Anal Mach Intell 1987; 9(6): 752-765. DOI: 10.1109/TPAMI.1987.4767982.
  18. Rodriguez JJ, Aggarwal JK. Stochastic analysis of stereo quantization error. IEEE Trans Pattern Anal Mach Intell 1990; 12(5): 467-470. DOI: 10.1109/34.55106.
  19. Zhang Z. Determining the epipolar geometry and its uncertainty: A review. International Journal of Computer Vision 1998; 27(2): 161-195. DOI: 10.1023/A:1007941100561.
  20. Hartley RI, Zisserman A. Multiple view geometry in computer vision. 2nd ed. Cambridge: Cambridge University Press; 2004. ISBN: 978-0-521-54051-3.
  21. Kanatani K. Statistical optimization for geometric computation: Theory and practice. Mineola: Dover Publications; 2005. ISBN: 978-0-486-44308-9.
  22. Julier SJ. The scaled unscented transformation. Proceedings of the 2002 American Control Conference 2002; 6: 4555-4559. DOI: 10.1109/ACC.2002.1025369.
  23. Zhang W, Liu M, Zhao Z. Accuracy analysis of unscented transformation of several sampling strategies. Proc 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing 2009: 377-380. DOI: 10.1109/SNPD.2009.13.
  24. Sibley G, Sukhatme GS, Matthies LH. The iterated sigma point Kalman filter with applications to long range stereo. Proc Robotics: Science and Systems 2006. DOI: 10.15607/RSS.2006.II.034.
  25. Sakai A, Kuroda Y. Discriminative parameter training of unscented Kalman filter. IFAC Proceedings Volumes 2010; 43(18): 677-682. DOI: 10.3182/20100913-3-US-2015.00063.
  26. Turner R, Rasmussen CE. Model based learning of sigma points in unscented Kalman filtering. Neurocomputing 2012; 80: 47-53. DOI: 10.1016/j.neucom.2011.07.029.
  27. Gorevoy AV, Machikhin AS, Shurygin AV, Khokhlov DD, Naumov AA. 3D spatial measurements by means of prism-based endoscopic imaging system. Proc GraphiCon 2016: 253-256.
  28. Gorevoy AV, Machikhin AS. Uncertainty evaluation of geometric parameter measurements performed using prism-based optical system. Proc GraphiCon 2017; 197-201.
  29. Chiu A, Jones T, van Daalen CE. A comparison of linearisation and the unscented transform for computer vision applications. Proc Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech) 2016: 1-6. DOI: 10.1109/RoboMech.2016.7813159.
  30. Kannala J, Brandt SS. A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses. IEEE Trans Pattern Analysis and Machine Intelligence 2006; 28(8): 1335-1340. DOI: 10.1109/TPAMI.2006.153.
  31. Zhang Z. Flexible camera calibration by viewing a plane from unknown orientations. Proc 7th IEEE International Conference on Computer Vision 1999: 666-673. DOI: 10.1109/ICCV.1999.791289.
  32. Matsuzawa T. Camera calibration based on the principal rays model of imaging optical systems. J Opt Soc Am A 2017; 34(4): 624-639. DOI: 10.1364/JOSAA.34.000624.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20