Summary

Proceedings of the 2012 International Symposium on Nonlinear Theory and its Applications

2012

Session Number:A2L-A

Session:

Number:74

An Improved Pose Estimation Algorithm for Nearly Coplanar Points

Haiwei Yang,  Fei Wang,  Jizhong Zhao,  Huan Fu,  Yongjian He,  

pp.74-77

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.1.74

PDF download (690.6KB)

Summary:
To uniquely determine the position and orientation of a calibrated camera from a single image with respect to known scene structure, pose estimation algorithms have been developed. However, these algorithms usually suffer from pose ambiguity problem. When all the object points are coplanar, algorithms have been presented to solve this problem. In this paper, we show that pose ambiguity also exists for non-coplanar object points, especially for nearly coplanar points. Based on an analysis of the cause of pose ambiguity for nearly coplanar points, we proposed an improved algorithm to solve this problem. Simulation results and experiments on real images demonstrate the effectiveness of our proposed pose estimation algorithm.

References:

[1] D.Oberkampf, D.F.DeMenthon, and L.S. Davis, Iterative Pose Estimation Using Coplanar Feature Points. Computer Vision and Image Understanding, vol.63, no.3, pp. 495-511, 1996.

[2] G.Schweighofer and A.Pinz. Robust Pose Estimation from a Planar Target. IEEE Trans. Pattern Analysis and Machine Intelligence, vol.28, no.12, pp.2024-2030, 2006.

[3] C.Lu,G.Hager,and E.Mjolsness, Fast and Globally Convergent Pose Estimation from Video Images. IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22,no.6,pp.610-622, 2000.

[4] V. Lepetit, F. Moreno-Noguer, and P. Fua. EPnP: An accurate O(n) solution to the PnP problem. Int. Journal of Computer Vision, 81(2):155-166, 2008.

[5] A.Ansar, K.Daniilidis. Linear pose estimation from points or lines. IEEE Transactions on Pattern Analysis and Machine Intelligence,25(5), 578-589,2003.

[6] P.D.Fiore.Efficient linear solution of exterior orientation. orientation.IEEE Transactions on Pattern Analysis and Machine Intelligence,23(2), 140-148, 2001.

[7] S.Gold, A.Rangarajan, C.Lu, S.Pappu and E.Mjolsness. New algorithms for 2D and 3D point matching: Pose estimation and correspondence. Pattern Recognition, 31(8):1019-1031.1998.

[8] A.Joel,H.Stergios, I.Roumeliotis. A direct least-squares (dls) solution for PnP. Int. Conf. on Computer Vision, Barcelona, Spain, November 6-13, 2011.

[9] Y. Wu and Z. Hu. PnP problem revisited. Journal of Mathematical Imaging and Vision, 24(1):131-141, Jan. 2006.