Skip to main content
SpringerLink
Log in

Camera pose estimation from lines: a fast, robust and general method

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In this paper, we revisit the perspective-n-line problem and propose a closed-form solution that is fast, robust and generally applicable. Our main idea is to formulate the pose estimation problem into an optimal problem. Our method only needs to solve a fifteenth-order and a fourth-order univariate polynomial, respectively, which makes the processes more easily understood and significantly improves the performance. Experiment results show that our method offers accuracy and precision comparable or better than existing state-of-the-art methods, but with significantly lower computational cost. This superior computational efficiency is particularly suitable for real applications.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. The source code of the proposed method (SR\(\hbox {P}n\hbox {L}\)) can be downloaded from https://sites.google.com/view/ping-wang-homepage.

References

  1. Abdelaziz, Y.I.: Direct linear transformation from comparator coordinates in close-range photogrammetry. In: Asp Symposium on Close-Range Photogrammetry in Illinois (1971)

  2. Ansar, A., Daniilidis, K.: Linear pose estimation from points or lines. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 578–589 (2003)

    Article  MATH  Google Scholar 

  3. Atiya, S., Hager, G.D.: Real-time vision-based robot localization. IEEE Trans. Rob. Autom. 9(6), 785–800 (1993)

    Article  Google Scholar 

  4. Billinghurst, M., Clark, A., Lee, G., et al.: A survey of augmented reality. Found. Trends® Human–Comput. Interact. 8(2–3), 73–272 (2015)

  5. Foulds, L.R.: Optimization Techniques: an Introduction. Springer (2012)

  6. Caglioti, V.: The planar three-line junction perspective problem with application to the recognition of polygonal patterns. Pattern Recognit. 26(11), 1603–1618 (1993)

    Article  Google Scholar 

  7. Chen, H.H.: Pose determination from line-to-plane correspondences: existence condition and closed-form solutions. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 530–541 (1991)

    Article  Google Scholar 

  8. Dhome, M., Richetin, M., Lapreste, J.T.: Determination of the attitude of 3D objects from a single perspective view. IEEE Trans. Pattern Anal. Mach. Intell. 11(12), 1265–1278 (1989)

    Article  Google Scholar 

  9. Dornaika, F., Garcia, C.: Pose estimation using point and line correspondences. Real Time Imaging 5(3), 215–230 (1999)

    Article  Google Scholar 

  10. Flannery, B.P., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, Cambridge (1987)

    MATH  Google Scholar 

  11. Gander, W.: Least Squares Fit of Point Clouds. Springer, Berlin (1997)

    Book  Google Scholar 

  12. Hesch, J.A., Roumeliotis, S.I.: A direct least-squares (DLS) method for P\(n\)P. In: Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 383–390. IEEE (2011)

  13. Horaud, R.: Iterative Pose Computation from Line Correspondences. Elsevier, Amsterdam (1999)

    MATH  Google Scholar 

  14. Kneip, L., Li, H., Seo, Y.: UPnP: an optimal \(O(n)\) solution to the absolute pose problem with universal applicability. In: European Conference on Computer Vision, pp. 127–142. Springer (2014)

  15. Kukelova, Z., Bujnak, M., Pajdla, T.: Automatic generator of minimal problem solvers. In: European Conference on Computer Vision, pp. 302–315 (2008)

  16. Kumar, R., Hanson, A.R.: Robust Methods for Estimating Pose and a Sensitivity Analysis. Academic Press, Cambridge (1994)

    Google Scholar 

  17. Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate \(O(n)\) solution to the P\(n\)P problem. Int. J. Comput. Vis. 81(2), 155 (2009)

    Article  Google Scholar 

  18. Li, S., Xu, C., Xie, M.: A robust \(O(n)\) solution to the perspective-\(n\)-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1444–1450 (2012)

    Article  Google Scholar 

  19. Lu, C.P., Hager, G.D., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Trans. Pattern Anal. Mach. Intell. 22(6), 610–622 (2000)

    Article  Google Scholar 

  20. Mirzaei, F.M., Roumeliotis, S.I.: Globally optimal pose estimation from line correspondences. In: IEEE International Conference on Robotics and Automation, pp. 5581–5588 (2011)

  21. Mourikis, A.I., Trawny, N., Roumeliotis, S.I., Johnson, A.E., Ansar, A., Matthies, L.: Vision-aided inertial navigation for spacecraft entry, descent, and landing. IEEE Trans. Robot. 25(2), 264–280 (2009)

    Article  Google Scholar 

  22. Nakano, G.: Globally optimal DLS method for P\(n\)P problem with Cayley parameterization. In: BMVC, pp. 78–1 (2015)

  23. Přibyl, B., Zemčík, P., Čadík, M.: Camera pose estimation from lines using Plücker coordinates. In: Proceedings of the British Machine Vision Conference, pp. 45.1–45.12 (2015)

  24. Qin, L.J., Feng, Z.: A new method for pose estimation from line correspondences. Acta Autom. Sin. 34(2), 130–134 (2008)

    Article  MathSciNet  Google Scholar 

  25. Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)

  26. Silva, M., Ferreira, R., Gaspar, J.: Camera calibration using a color-depth camera: points and lines based DLT including radial distortion. In: Proceedings of IROS Workshop in Color-Depth Camera Fusion in Robotics (2012)

  27. Wang, P., Xu, G., Cheng, Y., Yu, Q.: A simple, robust and fast method for the perspective-n-point problem. Pattern Recognit. Lett. 108, 31–37 (2018)

    Article  Google Scholar 

  28. Xu, C., Zhang, L., Cheng, L., Koch, R.: Pose estimation from line correspondences: a complete analysis and a series of solutions. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1209–1222 (2017)

    Article  Google Scholar 

  29. Zhang, L., Koch, R.: Hand-held monocular slam based on line segments. In: Machine Vision and Image Processing Conference, pp. 7–14 (2011)

  30. Zhang, L., Xu, C., Lee, K.M., Koch, R.: Robust and efficient pose estimation from line correspondences. In: Asian Conference on Computer Vision, pp. 217–230 (2012)

  31. Zheng, Y., Kuang, Y., Sugimoto, S., Åström, K., Okutomi, M.: Revisiting the P\(n\)P problem: a fast, general and optimal solution. In: Computer Vision (ICCV), 2013 IEEE International Conference on, pp. 2344–2351. IEEE (2013)

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 61473148), and the Foundation of China Shipbuilding Industry Company Limited (No. 6141B0405103).

Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Ping Wang

  2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Ping Wang, Guili Xu & Qida Yu

  3. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Yuehua Cheng

Authors
  1. Ping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guili Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuehua Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qida Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guili Xu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, P., Xu, G., Cheng, Y. et al. Camera pose estimation from lines: a fast, robust and general method. Machine Vision and Applications 30, 603–614 (2019). https://doi.org/10.1007/s00138-019-01012-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-019-01012-0

Keywords

Search

Navigation