Skip to main content

Experimental Quantification of Sensor-Based Stereocameras’ Extrinsic Parameters Calibration

  • Conference paper
  • First Online:
Computer Vision & Laser Vibrometry, Volume 6 (SEM 2023)

Abstract

Three-dimensional digital image correlation (3D-DIC) has shown to be a powerful tool to extract full-field displacement and deformations of structures using a series of synchronized stereo images. Before performing 3D-DIC measurements, the stereovision system must be calibrated to determine the relative position of the two cameras. Traditionally, this has been done by taking pictures of a calibration target whose dimensions are well known. Because the calibration target must be as large as the inspected object to properly calibrate the entire measurement volume (i.e., large-area calibration), for large fields of view, the calibration procedure becomes a challenge. This research aims at reducing the complexity of large-area calibration by measuring the extrinsic parameters of the stereovision system using a suite of sensors. In particular, three inertial measurement units and a laser distance meter are used to measure the cameras’ relative position and orientation in space. In this chapter, the performance of the proposed sensor-based extrinsic calibration is compared with the traditional image-based calibration method. Laboratory tests show that the extrinsic parameters computed with the sensor-based method can be used for performing a 3D-DIC analysis that yields an error below 5% when the results are compared with the displacement retrieved using a traditional approach. The results of this study demonstrate that the proposed sensor-based calibration approach is a valid and a faster alternative to traditional image-based calibration methods. The sensor-based calibration has the potential to expand the use of 3D-DIC by making it more suitable for large-scale applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sutton M.A., Orteu J.-J., Schreier, H.W.: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications, H. Schreier, J.-J. Orteu, M.A. Sutton (eds.). Boston: Springer US (2009)

    Google Scholar 

  2. Niezrecki, C., Baqersad, J., Sabato, A.: Digital image correlation techniques for NDE and SHM. In: Ida, N., Meyendorf, N. (eds.) Handbook of Advanced Nondestructive Evaluation, pp. 1545–1590. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  3. Zhang, D.Z.Z., Zhang, J., Peng, Z.: Improved robust and accurate camera calibration method used for machine vision application. Opt. Eng. 47(11), 117201 (2008)

    Article  Google Scholar 

  4. Remondino, F., Fraser, C.: Digital camera calibration methods: considerations and comparisons. Ine. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36, 11/30 (2005)

    Google Scholar 

  5. Reu, P.: Hidden components of DIC: calibration and shape function – part 1. Exp. Tech. 36(2), 3–5 (2012). https://doi.org/10.1111/j.1747-1567.2012.00821.x

    Article  Google Scholar 

  6. Feng, W., Zhilong, S., Yongsheng, H., Haibo, L., Qifeng, Y., Shaoping, L., Dongsheng, Z.: Inertial measurement unit aided extrinsic parameters calibration for stereo vision systems. Opt. Lasers Eng. 134, 106252 (2020). https://doi.org/10.1016/j.optlaseng.2020.106252

    Article  Google Scholar 

  7. Kumar, D., Chiang, C.-H., Lin, Y.-C.: Experimental vibration analysis of large structures using 3D DIC technique with a novel calibration method. J. Civil. Struct. Health Monit. 12, 391 (2022). https://doi.org/10.1007/s13349-022-00549-5

    Article  Google Scholar 

  8. Sabato, A., Valente, N.A., Niezrecki, C.: Development of a camera localization system for three-dimensional digital image correlation camera triangulation. IEEE Sensors J. 20(19), 11518–11526 (2020). https://doi.org/10.1109/jsen.2020.2997774

    Article  Google Scholar 

  9. Sabato A., Niezrecki C.: Development of an IMU-radar sensor board for three-dimensional digital image correlation camera triangulation. In: Proc. SPIE (2019), vol. 10972. https://doi.org/10.1117/12.2515081

  10. Bottalico F., Valente N.A., Dabetwar S., Jerath K., Luo Y., Niezrecki C., Sabato A.: A sensor-based calibration system for three-dimensional digital image correlation. In: Proc. SPIE (2022), vol. 12048. https://doi.org/10.1117/12.2612106

  11. Corke, P.I.: A simple and systematic approach to assigning Denavit–Hartenberg parameters. IEEE Trans. Robot. 23(3), 590–594 (2007). https://doi.org/10.1109/TRO.2007.896765

    Article  Google Scholar 

  12. puA1600-60uc - Basler pulse. https://www.baslerweb.com/en/products/cameras/area-scan-cameras/pulse/pua1600-60uc/. Accessed 22 Oct 2022

  13. LPMS-IG1 Series: High Precision 9-Axis Inertial Measurement Unit (IMU) / AHRS with USB / CAN / RS232 / RS485 Connectivity and Optional GPS Receiver. https://lp-research.com/9-axis-imu-with-gps-receiver-series/. Accessed 22 Oct 2022

  14. 40m Professional Width Measuring Sensor. https://www.jrt-measure.com/long-range-distance-sensor/57660459.html. Accessed 22 Oct 2022

  15. Turner D.Z.: Digital Image Correlation Engine (DICe) Reference Manual, Sandia Report, SAND2015-10606 O (2015). [Online]. Available: https://dicengine.github.io/dice/. Accessed 22 Oct 2022

Download references

Acknowledgments

This work was supported by the US National Science Foundation (NSF) under award number 2018992, “MRI: Development of a calibration system for stereophotogrammetry to enable large-scale measurement and monitoring.” The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement, by the funding agency.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabio Bottalico .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Society for Experimental Mechanics, Inc

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bottalico, F., Niezrecki, C., Jerath, K., Luo, Y., Sabato, A. (2024). Experimental Quantification of Sensor-Based Stereocameras’ Extrinsic Parameters Calibration. In: Baqersad, J., Di Maio, D. (eds) Computer Vision & Laser Vibrometry, Volume 6. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-34910-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34910-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34909-6

  • Online ISBN: 978-3-031-34910-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics