Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: an accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81, 155 (2009). https://doi.org/10.1007/s11263-008-0152-6
Choi, S.-I., Park, S.-Y.: A new 2-point absolute pose estimation algorithm under planar motion. Adv. Robot. 29, 1005–1013 (2015). https://doi.org/10.1080/01691864.2015.1024285
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)
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)
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). https://doi.org/10.1109/TPAMI.2012.41
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). https://doi.org/10.1587/transinf.E96.D.1525
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)
Ferraz, L., Binefa, X., Moreno-Noguer, F.: Leveraging feature uncertainty in the PnP problem. Presented at the (2014)
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). https://doi.org/10.5194/isprs-annals-III-3-131-2016
Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vis. 60, 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110, 346–359 (2008). https://doi.org/10.1016/j.cviu.2007.09.014
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)
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)
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). https://doi.org/10.1145/358669.358692
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)
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). https://doi.org/10.1109/TPAMI.2003.1217599
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)
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)
Pribyl, B., Zemcík, P., Cadík, M.: Camera pose estimation from lines using Plücker coordinates. arXiv:1608.02824 [cs]. 45.1-45.12. https://doi.org/10.5244/C.29.45 (2015)
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). https://doi.org/10.1109/34.166625
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). https://doi.org/10.1109/34.862199
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)
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)
Hmam, H., Kim, J.: Optimal non-iterative pose estimation via convex relaxation. Image Vis. Comput. 28, 1515–1523 (2010). https://doi.org/10.1016/j.imavis.2010.03.005
Larsson, V., Fredriksson, J., Toft, C., Kahl, F.: Outlier rejection for absolute pose estimation with known orientation. Presented at the (2016)
Förstner, W.: Minimal representations for uncertainty and estimation in projective spaces. In: Computer Vision—ACCV 2010, pp. 619–632. Springer, Berlin (2010)
Schweighofer, G., Pinz, A.: Globally optimal O(n) solution to the PnP problem for general camera models. Presented at the (2008)
VLFeat - Home, http://www.vlfeat.org/index.html
Nister, D.: An efficient solution to the five-point relative pose problem. IEEE Trans. Pattern Anal. Mach. Intell. 26, 756–770 (2004). https://doi.org/10.1109/TPAMI.2004.17
Acknowledgments
Nagarajan Pitchandi is supported by the University Grants Commission, India- National Fellowship Scheme F./2016–17/ NFO-2015–17-OBC-TAM-33559.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pitchandi, N., Subramanian, S.P. Image Uncertainty-Based Absolute Camera Pose Estimation with Fibonacci Outlier Elimination. J Intell Robot Syst 96, 65–81 (2019). https://doi.org/10.1007/s10846-019-00985-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-019-00985-4