Robust Accurate Extrinsic Calibration of Static Non-overlapping Cameras

  • Andreas Robinson
  • Mikael Persson
  • Michael Felsberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10425)

Abstract

An increasing number of robots and autonomous vehicles are equipped with multiple cameras to achieve surround-view sensing. The estimation of their relative poses, also known as extrinsic parameter calibration, is a challenging problem, particularly in the non-overlapping case. We present a simple and novel extrinsic calibration method based on standard components that performs favorably to existing approaches. We further propose a framework for predicting the performance of different calibration configurations and intuitive error metrics. This makes selecting a good camera configuration straightforward. We evaluate on rendered synthetic images and show good results as measured by angular and absolute pose differences, as well as the reprojection error distributions.

Notes

Acknowledgement

This work was funded in part by Vinnova, Sweden’s innovation agency, through grant iQmatic, Daimler AG, EC’s Horizon 2020 Programme, grant agreement CENTAURO and The Swedish Research Council through a framework grant for the project Energy Minimization for Computational Cameras (2014-6227).

References

  1. 1.
    Agrawal, A.: Extrinsic camera calibration without a direct view using spherical mirror. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2368–2375 (2013)Google Scholar
  2. 2.
    Antone, M., Teller, S.: Scalable extrinsic calibration of omni-directional image networks. Int. J. Comput. Vision 49(2), 143–174 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Bradski, G., et al.: The OpenCV library. Dr. Dobbs J. 25(11), 120–126 (2000)Google Scholar
  4. 4.
    Carrera, G., Angeli, A., Davison, A.J.: Slam-based automatic extrinsic calibration of a multi-camera rig. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 2652–2659. IEEE (2011)Google Scholar
  5. 5.
    Dong, S., Shao, X., Kang, X., Yang, F., He, X.: Extrinsic calibration of a non-overlapping camera network based on close-range photogrammetry. Appl. Opt. 55(23), 6363–6370 (2016)CrossRefGoogle Scholar
  6. 6.
    Esquivel, S., Woelk, F., Koch, R.: Calibration of a multi-camera rig from non-overlapping views. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 82–91. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74936-3_9 CrossRefGoogle Scholar
  7. 7.
    Foundation, B.: Blender version 2.78c (2017). https://www.blender.org/
  8. 8.
    Hedborg, J., Forssén, P.-E., Felsberg, M.: Fast and accurate structure and motion estimation. In: Bebis, G., et al. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 211–222. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10331-5_20 CrossRefGoogle Scholar
  9. 9.
    Hesch, J.A., Mourikis, A.I., Roumeliotis, S.I.: Extrinsic camera calibration using multiple reflections. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 311–325. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_23 CrossRefGoogle Scholar
  10. 10.
    Kumar, R.K., Ilie, A., Frahm, J.M., Pollefeys, M.: Simple calibration of non-overlapping cameras with a mirror. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–7. IEEE (2008)Google Scholar
  11. 11.
    Lébraly, P., Deymier, C., Ait-Aider, O., Royer, E., Dhome, M.: Flexible extrinsic calibration of non-overlapping cameras using a planar mirror: application to vision-based robotics. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5640–5647. IEEE (2010)Google Scholar
  12. 12.
    Liu, Q., Sun, J., Liu, Z., Zhang, G.: Global calibration method of multi-sensor vision system using skew laser lines. Chin. J. Mech. Eng. 25(2), 405–410 (2012)CrossRefGoogle Scholar
  13. 13.
    Liu, Z., Zhang, G., Wei, Z., Sun, J.: Novel calibration method for non-overlapping multiple vision sensors based on 1d target. Opt. Lasers Eng. 49(4), 570–577 (2011)CrossRefGoogle Scholar
  14. 14.
    Long, G., Kneip, L., Li, X., Zhang, X., Yu, Q.: Simplified mirror-based camera pose computation via rotation averaging. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1247–1255 (2015)Google Scholar
  15. 15.
    Maddern, W.: Industry solutions: autonomous vehicle driven by vision. Vis. Syst. Des. 22(2), February 2017. http://www.vision-systems.com/articles/print/volume-22/issue-2/features/industry-solutions-autonomous-vehicle-driven-by-vision.html
  16. 16.
    Pagel, F.: Extrinsic self-calibration of multiple cameras with non-overlapping views in vehicles. In: IS&T/SPIE Electronic Imaging, p. 902606. International Society for Optics and Photonics (2014)Google Scholar
  17. 17.
    Rodrigues, R., Barreto, J.P., Nunes, U.: Camera pose estimation using images of planar mirror reflections. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 382–395. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_28 CrossRefGoogle Scholar
  18. 18.
    Sturm, P., Bonfort, T.: How to compute the pose of an object without a direct view? In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3852, pp. 21–31. Springer, Heidelberg (2006). doi:10.1007/11612704_3 CrossRefGoogle Scholar
  19. 19.
    Takahashi, K., Nobuhara, S., Matsuyama, T.: A new mirror-based extrinsic camera calibration using an orthogonality constraint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1051–1058. IEEE (2012)Google Scholar
  20. 20.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andreas Robinson
    • 1
  • Mikael Persson
    • 1
  • Michael Felsberg
    • 1
  1. 1.Linköping UniversityLinköpingSweden

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