Abstract
This paper addresses the camera pose estimation problem from 3D lines and their 2D projections, known as the perspective-n-line (PnL) problem. Although many successful solutions have been presented, it is still a challenging to optimize both computational complexity and accuracy at the same time. In our work, we parameterize the rotation by using the Cayley–Gibbs–Rodriguez (CGR) parameterization and formulate the PnL problem into a polynomial system solving problem. Instead of the Gröbner basis method, which may encounter numeric problems, we seek for an efficient and stability technique—the hidden variable method—to solve the polynomial system and polish the solution via the Gauss–Newton method. The performance of our method is evaluated by using simulations and real images, and results demonstrate that our method offers accuracy and precision comparable or better than existing state-of-the-art methods, but with significantly lower computational cost.








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The C++ version will be published upon completion.
All source codes can be downloaded from https://sites.google.com/view/ping-wang-homepage.
References
Abdelaziz, Y.I.: Direct linear transformation from comparator coordinates in close-range photogrammetry. In: Asp Symposium on Close-Range Photogrammetry in Illinois (1971)
Ansar, A., Daniilidis, K.: Linear pose estimation from points or lines. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 578–589 (2003)
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., MacIntyre, B.: Recent advances in augmented reality. Comput. Graph. Appl. IEEE 21(6), 34–47 (2001)
Azuma, R.T., et al.: A survey of augmented reality. Presence 6(4), 355–385 (1997)
Brezov, D.S., Mladenova, C.D., Mladenov, I.M.: New perspective on the gimbal lock problem. In: American Institute of Physics Conference Series (2013)
Bronson, R., Costa, G.B.: An Introduction to Optimization (2009)
Caglioti, V.: The planar three-line junction perspective problem with application to the recognition of polygonal patterns. Pattern Recognit. 26(11), 1603–1618 (1993)
Cao, M.W., Jia, W., Zhao, Y., Li, S.J., Liu, X.P.: Fast and robust absolute camera pose estimation with known focal length. Neural. Comput. Appl. 29(5), 1383 (2018)
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)
Dani, A.P., Fischer, N.R., Dixon, W.E.: Single camera structure and motion. IEEE Trans. Autom. Control 57(1), 238–243 (2012)
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)
Engelhard, N., Endres, F., Hess, J., Sturm, J., Burgard, W.: Real-time 3d visual slam with a hand-held RGB-D camera. In: Proceedings of the RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, Vasteras, Sweden, vol. 180 (2011)
Ferraz, L., Binefa, X., Moreno-Noguer, F.: Very fast solution to the PnP problem with algebraic outlier rejection. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 501–508. IEEE (2014)
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(6), 381–395 (1981)
Gander, W.: Least Squares Fit of Point Clouds. Springer, Berlin (1997)
Han, P., Zhao, G.: Line-based initialization method for mobile augmented reality in aircraft assembly. Vis. Comput. 33(9), 1185–1196 (2017)
Hesch, J.A., Roumeliotis, S.I.: A direct least-squares (DLS) method for PnP. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 383–390. IEEE (2011)
Jafari, M., Yayli, Y.: Generalized quaternion and rotation in 3-space e (3-alfa, beta). Physics (2012)
Kangni, F., Laganiere, R.: Orientation and pose recovery from spherical panoramas. In: IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007. pp. 1–8. IEEE (2007)
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, Berlin (2014)
Kukelova, Z., Bujnak, M., Pajdla, T.: Automatic generator of minimal problem solvers. In: Computer Vision—ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, October 12–18, 2008, Proceedings, Part III (2008)
Kumar, R., Hanson, A.R.: Robust Methods for Estimating Pose and a Sensitivity Analysis. Academic Press Inc, Cambridge (1994)
Lategahn, H., Geiger, A., Kitt, B.: Visual slam for autonomous ground vehicles. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1732–1737. IEEE (2011)
Lepetit, V., Moreno-Noguer, F., Fua, P.: EPnP: An accurate O(n) solution to the PnP problem. Int. J. Comput. Vis. 81(2), 155 (2009)
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)
Liu, Y.: Determination of camera location from 2-d to 3-d line and point correspondences. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 28–37 (1990)
Liu, Y., Chen, X., Gu, T., Zhang, Y., Xing, G.: Real-time camera pose estimation via line tracking. Vis. Comput. 34(6–8), 899–909 (2018)
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)
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)
Nakano, G.: Globally optimal DLS method for PnP problem with Cayley parameterization. In: BMVC, pp. 78–1 (2015)
Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, Cambridge (1986)
Přibyl, B., Zemčlk, P., Čadlk, M.: Camera pose estimation from lines using plücker coordinates (2016)
Přibyl, B., Zemčlk, P., Čadlk, M.: Absolute pose estimation from line correspondences using direct linear transformation. Comput. Vis. Image Underst. 161, 130–144 (2017)
Ryan, J., Hubbard, A., Box, J., Todd, J., Christoffersen, P., Carr, J., Holt, T., Snooke, N.: Uav photogrammetry and structure from motion to assess calving dynamics at store glacier, a large outlet draining the greenland ice sheet. Cryosphere 9(1), 1–11 (2015)
Silva, M., Ferreira, R., Gaspar, J.: Camera calibration using a color-depth camera: points and lines based DLT including radial distortion (2013)
Urban, S., Leitloff, J., Hinz, S.: MLPnP —a real-time maximum likelihood solution to the perspective-n-point problem. ISPRS J. Photogram. Rem. Sens. 3(3), 131–138 (2016)
Visual, S.: IROS 2014: Robots descend on Chicago. In: IEEE Robotics and Automation Magazine (2015)
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)
Wang, P., Xu, G., Cheng, Y., Yu, Q.: Camera pose estimation from lines: a fast, robust and general method. Mach. Vis. Appl. 30, 603–614 (2019)
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(99), 1–1 (2017)
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)
Zhang, Y., Xin, L., Liu, H., Yang, S.: Probabilistic approach for maximum likelihood estimation of pose using lines. IET Comput. Vis. 10(6), 475–482 (2016)
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 (ICCV), pp. 2344–2351. IEEE (2013)
Zhou, L., Kaess, M.: An efficient and accurate algorithm for the perspective-n-point problem. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2019)
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This work was supported by the National Natural Science Foundation of China (No. 62001198).
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Appendices
Appendix A: Coefficients of Eqs. (17)–(19)
Appendix B: Coefficients of Eqs.(20)–(22)
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Wang, P., Chou, Y., An, A. et al. Solving the PnL problem using the hidden variable method: an accurate and efficient solution. Vis Comput 38, 95–106 (2022). https://doi.org/10.1007/s00371-020-02004-2
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DOI: https://doi.org/10.1007/s00371-020-02004-2