Image Uncertainty-Based Absolute Camera Pose Estimation with Fibonacci Outlier Elimination

  • Nagarajan PitchandiEmail author
  • Saravana Perumaal Subramanian


Estimating the pose of a camera is a vital requirement in real-world applications like virtual reality, structure from motion, vision-assisted robot localization and manipulation. The existing Perspective-n-Point (PnP) based pose estimation algorithms have poor accuracy in presence of noise and outliers. Hence, they are combined with the Random Sample Consensus strategies to eliminate the outliers prior to pose estimation and to produce accurate results at the expense of computation time. With this concern, an Image Uncertainty-based Perspective-n-Point (IUPnP) with Fibonacci-based outlier rejection is proposed to accurately estimate the absolute pose of a calibrated camera with a minimum computation load. The uncertainties of the spherically normalized camera coordinates are formulated in the tangent space of the camera coordinate system and the initial pose is estimated using Singular Value Decomposition. The correspondences with tangent space residual exceeding the threshold values, are classified as outliers and then rejected iteratively. In order to prevent the inlier rejections and to improve the pose estimates, the threshold values are updated eventually using the Fibonacci technique. Finally, the estimated pose values are refined, using Gauss-Newton optimization. The proposed IUPnP algorithm is tested with synthetic data and real image data to validate its performance by comparing with the existing PnP algorithms in terms of accuracy. The results show that the proposed technique produces better pose estimates for correspondences with 50% outliers, than the state-of-art techniques do.


Camera pose estimation Perspective-n-Point algorithm Outlier rejection Image Uncertainty 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



Nagarajan Pitchandi is supported by the University Grants Commission, India- National Fellowship Scheme F./2016–17/ NFO-2015–17-OBC-TAM-33559.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81, 155 (2009). CrossRefGoogle Scholar
  2. 2.
    Choi, S.-I., Park, S.-Y.: A new 2-point absolute pose estimation algorithm under planar motion. Adv. Robot. 29, 1005–1013 (2015). CrossRefGoogle Scholar
  3. 3.
    Nöll, T., Pagani, A., Stricker, D.: Real-time camera pose estimation using correspondences with high outlier ratios. In: VISAPP 2010: International Conference on Computer Vision Theory and Applications, pp. 381–386 (2010)Google Scholar
  4. 4.
    Kneip, L., Scaramuzza, D., Siegwart, R.: A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation. In: CVPR 2011, pp. 2969–2976 (2011)Google Scholar
  5. 5.
    Li, S., Xu, C., Xie, M.: A robust O(n) solution to the perspective-n-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1444–1450 (2012). CrossRefGoogle Scholar
  6. 6.
    Zheng, Y., Sugimoto, S., Okutomi, M.: ASPnP: an accurate and scalable solution to the perspective-n-point problem. IEICE Trans. Inf. Syst. E96.D, 1525–1535 (2013). CrossRefGoogle Scholar
  7. 7.
    Zheng, Y., Kuang, Y., Sugimoto, S., Åström, K., Okutomi, M.: Revisiting the PnP problem: a fast, general and optimal solution. In: 2013 IEEE International Conference on Computer Vision, pp. 2344–2351 (2013)Google Scholar
  8. 8.
    Ferraz, L., Binefa, X., Moreno-Noguer, F.: Leveraging feature uncertainty in the PnP problem. Presented at the (2014)Google Scholar
  9. 9.
    Urban, S., Leitloff, J., Hinz, S.: MLPnP—a real-time maximum likelihood solution to the perspective-n-point problem. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. III–3, 131–138 (2016). CrossRefGoogle Scholar
  10. 10.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 60, 91–110 (2004). CrossRefGoogle Scholar
  11. 11.
    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008). CrossRefGoogle Scholar
  12. 12.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005)Google Scholar
  13. 13.
    Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision—ECCV 2006, pp. 430–443. Springer, Berlin (2006)Google Scholar
  14. 14.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981). MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ferraz, L., Binefa, X., Moreno-Noguer, F.: Very fast solution to the PnP problem with algebraic outlier rejection. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 501–508 (2014)Google Scholar
  16. 16.
    Gao, X.-S., Hou, X.-R., Tang, J., Cheng, H.-F.: Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 25, 930–943 (2003). CrossRefGoogle Scholar
  17. 17.
    Triggs, B.: Camera pose and calibration from 4 or 5 known 3D points. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 278–284 (1999)Google Scholar
  18. 18.
    David, P., DeMenthon, D., Duraiswami, R., Samet, H.: Simultaneous pose and correspondence determination using line features. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings, vol. 2, pp. II-424–II-431 (2003)Google Scholar
  19. 19.
    Pribyl, B., Zemcík, P., Cadík, M.: Camera pose estimation from lines using Plücker coordinates. arXiv:1608.02824 [cs]. 45.1-45.12. (2015)
  20. 20.
    DeMenthon, D., Davis, L.S.: Exact and approximate solutions of the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 14, 1100–1105 (1992). CrossRefGoogle Scholar
  21. 21.
    Lu, C.P., Hager, G.D., Mjolsness, E.: Fast and globally convergent pose estimation from video images. IEEE Trans. Pattern Anal. Mach. Intell. 22, 610–622 (2000). CrossRefGoogle Scholar
  22. 22.
    Garro, V., Crosilla, F., Fusiello, A.: Solving the PnP problem with anisotropic orthogonal procrustes analysis. In: Visualization Transmission 2012 Second International Conference on 3D Imaging, Modeling, Processing, pp. 262–269 (2012)Google Scholar
  23. 23.
    Hesch, J.A., Roumeliotis, S.I.: A Direct Least-Squares (DLS) method for PnP. In: 2011 International Conference on Computer Vision, pp. 383–390 (2011)Google Scholar
  24. 24.
    Hmam, H., Kim, J.: Optimal non-iterative pose estimation via convex relaxation. Image Vis. Comput. 28, 1515–1523 (2010). CrossRefGoogle Scholar
  25. 25.
    Larsson, V., Fredriksson, J., Toft, C., Kahl, F.: Outlier rejection for absolute pose estimation with known orientation. Presented at the (2016)Google Scholar
  26. 26.
    Förstner, W.: Minimal representations for uncertainty and estimation in projective spaces. In: Computer Vision—ACCV 2010, pp. 619–632. Springer, Berlin (2010)Google Scholar
  27. 27.
    Schweighofer, G., Pinz, A.: Globally optimal O(n) solution to the PnP problem for general camera models. Presented at the (2008)Google Scholar
  28. 28.
  29. 29.
    Nister, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26, 756–770 (2004). CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Nagarajan Pitchandi
    • 1
    Email author
  • Saravana Perumaal Subramanian
    • 1
  1. 1.Department of Mechanical EngineeringThiagarajar College of EngineeringMaduraiIndia

Personalised recommendations