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Distributed One Dimensional Calibration and Localisation of a Camera Sensor Network

  • Brendan HalloranEmail author
  • Prashan Premaratne
  • Peter Vial
  • Inas Kadhim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)

Abstract

Metric calibration and localisation are crucial requirements for many higher-level robotic vision tasks, such as visual navigation and tracking. Furthermore, distributed algorithms are being increasingly used to create scalable camera sensor networks (CSN) which are resistant to node failure. We present a distributed algorithm for the calibration and localisation of a CSN. Our method involves a robust local calibration at each node using a 1D calibration object, consisting of collinear points moving about a single fixed point. Next, each node builds a vision graph and performs cluster-based bundle adjustment, utilising the structure of calibration object to produce pose estimates for its cluster. Finally, these estimates are brought to global consensus through Gaussian belief propagation. Experimental results validate our algorithm, showing that it has comparable performance to centralised algorithms, despite being distributed in nature.

Keywords

Camera calibration Localisation Distributed algorithms Gaussian belief propagation 

Notes

Acknowledgements

This research has been conducted with the support of the Australian Government Research Training Program Scholarship.

References

  1. 1.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  2. 2.
    Luong, Q.T., Faugeras, O.D.: Self-calibration of a moving camera from point correspondences and fundamental matrices. Int. J. Comput. Vision 22(3), 261–289 (1997)CrossRefGoogle Scholar
  3. 3.
    Zhang, Z.: Camera calibration with one-dimensional objects. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 892–899 (2004)CrossRefGoogle Scholar
  4. 4.
    Bickson, D.: Gaussian belief propagation: Theory and application. arXiv preprint arXiv:0811.2518 (2008)
  5. 5.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Burlington (2014)zbMATHGoogle Scholar
  6. 6.
    Weiss, Y., Freeman, W.T.: Correctness of belief propagation in Gaussian graphical models of arbitrary topology. Neural Comput. 13(10), 2173–2200 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Hammarstedt, P., Sturm, P., Heyden, A.: Degenerate cases and closed-form solutions for camera calibration with one-dimensional objects. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 317–324. IEEE (2005)Google Scholar
  8. 8.
    Wu, F., Hu, Z., Zhu, H.: Camera calibration with moving one-dimensional objects. Pattern Recogn. 38(5), 755–765 (2005)CrossRefGoogle Scholar
  9. 9.
    Qi, F., Li, Q., Luo, Y., Hu, D.: Camera calibration with one-dimensional objects moving under gravity. Pattern Recogn. 40(1), 343–345 (2007)CrossRefzbMATHGoogle Scholar
  10. 10.
    Qi, F., Li, Q., Luo, Y., Hu, D.: Constraints on general motions for camera calibration with one-dimensional objects. Pattern Recogn. 40(6), 1785–1792 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    De Franca, J.A., Stemmer, M.R., de M. Franca, M.B., Alves, E.G.: Revisiting zhang’s 1D calibration algorithm. Pattern Recogn. 43(3), 1180–1187 (2010)CrossRefzbMATHGoogle Scholar
  12. 12.
    Shi, K., Dong, Q., Wu, F.: Weighted similarity-invariant linear algorithm for camera calibration with rotating 1D objects. IEEE Trans. Image Process. 21(8), 3806–3812 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Wang, L., Wang, W., Shen, C., Duan, F.: A convex relaxation optimization algorithm for multi-camera calibration with 1D objects. Neurocomputing 215, 82–89 (2016)CrossRefGoogle Scholar
  14. 14.
    Kojima, Y., Fujii, T., Tanimoto, M.: New multiple-camera calibration method for a large number of cameras. In: Electronic Imaging 2005, pp. 156–163. International Society for Optics and Photonics (2005)Google Scholar
  15. 15.
    Wang, L., Wu, F., Hu, Z.: Multi-camera calibration with one-dimensional object under general motions. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–7. IEEE (2007)Google Scholar
  16. 16.
    De Franca, J.A., Stemmer, M.R., Franca, M.B.d.M, Piai, J.C.: A new robust algorithmic for multi-camera calibration with a 1D object under general motions without prior knowledge of any camera intrinsic parameter. Pattern Recogn. 45(10), 3636–3647 (2012)CrossRefzbMATHGoogle Scholar
  17. 17.
    Devarajan, D., Radke, R.J.: Distributed metric calibration of large camera networks. In: Proceedings of the 1st Workshop on Broadband Advanced Sensor Networks, vol. 3(4), pp. 5–24 (2004)Google Scholar
  18. 18.
    Devarajan, D., Radke, R.J., Chung, H.: Distributed metric calibration of ad hoc camera networks. ACM Trans. Sens. Netw. (TOSN) 2(3), 380–403 (2006)CrossRefGoogle Scholar
  19. 19.
    Devarajan, D., Radke, R.J.: Calibrating distributed camera networks using belief propagation. EURASIP J. Appl. Sig. Process. 2007(1), 221–222 (2007)Google Scholar
  20. 20.
    Devarajan, D., Cheng, Z., Radke, R.J.: Calibrating distributed camera networks. Proc. IEEE 96(10), 1625–1639 (2008)CrossRefGoogle Scholar
  21. 21.
    Tron, R., Vidal, R.: Distributed image-based 3D localization of camera sensor networks. In: Proceedings of the 48th IEEE Conference on Decision and Control, Held Jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009, pp. 901–908. IEEE (2009)Google Scholar
  22. 22.
    Tron, R., Vidal, R.: Distributed 3D localization of camera sensor networks from 2D image measurements. IEEE Trans. Autom. Control 59(12), 3325–3340 (2014)CrossRefzbMATHGoogle Scholar
  23. 23.
    Triggs, B.: Autocalibration from planar scenes. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 89–105. Springer, Heidelberg (1998). doi: 10.1007/BFb0055661 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Brendan Halloran
    • 1
    Email author
  • Prashan Premaratne
    • 1
  • Peter Vial
    • 1
  • Inas Kadhim
    • 1
  1. 1.University of WollongongWollongongAustralia

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