Calibration of Bi-prism Stereo Systems: A Model Free Approach

Authors

  • Supun Dissanayaka Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang
  • Pitikhate Sooraksa Robotics and AI, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang
  • Somyot Kaitwanidvilai Electrical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang
  • John Morris School of Industrial Education and Technology, King Mongkut’s Institute of Technology, Ladkrabang

DOI:

https://doi.org/10.55003/ETH.410302

Keywords:

Bi-prism, Stereo, Calibration, Monochromatic

Abstract

A simple stereo system can be constructed from a single camera using a prism in the optical path to provide the required two views of a system. The simplicity of these systems has several advantages, particularly if the target is an underwater robot, where compact size and ability to seal the optical components are key factors. However, dispersion by the prism, in addition to the lens distortion, makes calibration challenging. By using a model-free approach, we were able to calibrate a prism-based stereo system effectively. We also aimed to use readily available 45° prisms, which present significant dispersion in the system, but retain simplicity and reduce cost, compared to custom low angle prisms. Modern LEDs provide high intensity, low bandwidth light sources and we used a set of three sources, roughly centered on the RGB channels of a readily available commercial camera. Our system used a circular target pattern covering the binocularly visible region in the scene and collected sets of images at known distances, using three separate light sources. From these images, we generated two look-up tables, one for each pixel in the image and a disparity derived by matching corresponding points, Cp(u,v,du), which has three dimensions, and another look-up table, which has a single dimension, Cz(z), so are not quite large, and not beyond the memory capability of even small modern camera systems, but provide fast, O(1), lookup times, suitable for real-time systems. Our calibration strategy enables a simple stereo system built from a single camera to measure depths in a scene: the single camera requires no electronic synchronization and is built from a single, inexpensive, and readily available optical component – a right-angle prism.

References

S. Hussmann, T. Ringbeck and B. Hagebeuker, “A Performance Review of 3D TOF Vision Systems in Comparison to Stereo Vision Systems,” in Stereo vision, London, United Kingdom: IntechOpen Limited, 2008, ch. 7, pp. 103–120.

J. -J. Aguilar, F. Torres and M. Lope, “Stereo vision for 3d measurement: accuracy analysis, calibration and industrial applications,” Measurement, vol. 18, no. 4, pp. 193–200, 1996, doi: 10.1016/S0263-2241(96)00065-6.

K. B. Lim, W. L. Kee and D. Wang, “Virtual camera calibration and stereo correspondence of single-lens bi-prism stereovision system using geometrical approach,” Signal Processing: Image Communication, vol. 28, no. 9, pp. 1059–1071, 2013, doi: 10.1016/j.image.2013.08.002.

M. Wang, “Theoretical and experimental study on a new reconstruction model based on geometric optics for a single-lens biprism-based stereovision system,” Measurement Science and Technology, vol. 33, no. 8, 2022, Art. no. 085404, doi: 10.1088/1361-6501/ac6080.

C. C. Slama, “Introduction,” in Manual of photogrammetry, Falls Church, VA, USA: American Society of Photogrammetry, 1980, ch. 1, pp. 1–30.

J. Heikkila and O. Silv´en, “A four-step camera calibration procedure with implicit image correction,” in Proc. IEEE computer society conference on computer vision and pattern recognition, San Juan, PR, USA, 1997, pp. 1106–1112, doi: 10.1109/CVPR.1997.609468.

Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on pattern analysis and machine intelligence, vol. 22, no. 11, pp. 1330–1334, 2000, doi: 10.1109/34.888718.

J. Zhang, H. Yu, H. Deng, Z. Chai, M. Ma and X. Zhong, “A robust and rapid camera calibration method by one captured image,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 4112–4121, 2019, doi: 10.1109/TIM.2018.2884583.

R. Kala, “Perception in Autonomous Vehicles,” in On-road intelligent vehicles: Motion planning for intelligent transportation systems, Cambridge, MA, USA: Butterworth-Heinemann, 2016, ch. 3, pp. 36–58.

E. M. Mouaddib, R. Sagawa, T. Echigo and Y. Yagi, “Stereovision with a single camera and multiple mirrors,” in Proc. 2005 IEEE International Conference on Robotics and Automation, Barcelona, Spain, 2005, pp. 800–805, doi: 10.1109/ROBOT.2005.1570215.

K. Takahashi, S. Nobuhara and T. Matsuyama, “A new mirror-based extrinsic camera calibration using an orthogonality constraint,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012, pp. 1051–1058, doi: 10.1109/CVPR.2012.6247783.

J. Li, B. Zhang, X. Kang, W. Xu, G. Yang and L. Yang, “Single camera 3d digital image correlation using a polarized system,” Instruments and Experimental Techniques, vol. 61, no. 1, pp. 99–105, 2018, doi: 10.1134/S0020441218010050.

D. Lee and I. Kweon, “A novel stereo camera system by a biprism,” IEEE Transactions on Robotics and Automation, vol. 16, no. 5, pp. 528–541, 2000, doi: 10.1109/70.880803.

K. Genovese, L. Casaletto, J. A. Rayas, V. H. Flores and A. Martinez, “Stereo-digital image correlation (DIC) measurements with a single camera using a biprism,” Optics and Lasers in Engineering, vol. 51, no. 3, pp. 278–285, 2013, doi: 10.1016/j.optlaseng.2012.10.001.

B. Pan, D. Wu and Y. Xia, “An active imaging digital image correlation method for deformation measurement insensitive to ambient light,” Optics & Laser Technology, vol. 44, no. 1, pp. 204–209, 2012, doi: 10.1016/j.optlastec.2011.06.019.

B. Pan, L. Yu and Q. Zhang, “Review of single-camera stereo-digital image correlation techniques for full-field 3d shape and deformation measurement,” Science China Technological Sciences, vol. 61, pp. 2–20, 2018, doi: 10.1007/s11431-017-9090-x.

K. B. Lim and B. Qian, “Biprism distortion modeling and calibration for a single-lens stereovision system,” Journal of the Optical Society of America A, vol. 33, no. 11, pp. 2213–2224, 2016, doi: 10.1364/JOSAA.33.002213.

L. Yu and B. Pan, “Single-camera stereo-digital image correlation with a four-mirror adapter: optimized design and validation,” Optics and Lasers in Engineering, vol. 87, pp. 120–128, 2016, doi: 10.1016/j.optlaseng.2016.03.014.

L. Wu, J. Zhu and H. Xie, “Single-lens 3d digital image correlation system based on a bilateral telecentric lens and a bi-prism: validation and application,” Applied Optics, vol. 54, no. 26, pp. 7842–7850, 2015, doi: 10.1364/AO.54.007842.

Z. Hui, Z. Liyan, W. Hongtao and C. Jianfu, “Surface measurement based on instantaneous random illumination,” Chinese Journal of Aeronautics, vol. 22, no. 3, pp. 316–324, 2009, doi: 10.1016/S1000-9361(08)60105-3.

J. Morris and G. Gimel’farb, “High resolution stereo hardware implementation,” US Patent US20110091096A1, May, 4,2009.

D. B. Murphy and M. W. Davidson, “Light and Color,” in Fundamentals of light microscopy and electronic imaging, Hoboken, NJ, USA: John Wiley & Sons, 2012, ch. 2, pp. 21–32.

K. Zhu and B. Pan, “Panoramic/dual-surface digital image correlation measurement using a single camera,” sensors, vol. 22, no. 9, 2022, Art. no. 3266, doi: 10.3390/s22093266.

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Published

2024-09-30

How to Cite

[1]
S. . Dissanayaka, P. . Sooraksa, S. Kaitwanidvilai, and J. . Morris, “Calibration of Bi-prism Stereo Systems: A Model Free Approach”, Eng. & Technol. Horiz., vol. 41, no. 3, p. 410302, Sep. 2024.

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Research Articles