Estimation of pose parameters from a set of least square objective functions

• Autorzy: Hati S.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we have attempted to solve the pose estimation problem for a 3-dimensional object by independently estimating the pose parameters through the minimization of a set of objective, functions, using Gauss approximation techniques for least squares optimization. In our implementation, the 3-D object is assumed to have three degrees of freedom on a flat surface, which is typical of automated visual inspection applications. However, the solution can be also extended to greater degrees of freedom. We have is shown that the pose can be estimated by only considering the x-coordinates of the known vertices in the projected space, but the same is not true if we consider the y-coordinates alone. We propose a set of modified objective functions from which it is possible to find the pose parameters. The parameters have been determined in noisy conditions under 20-dB and 40-dB SNR values and the robustness of the estimators is confirmed.

Słowa kluczowe

EN

pose estimation; camera calibration; least square estimation; robust estimator

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2008
• Tom: Vol. 17, No. 3
• Strony: 299--312
• Opis fizyczny: Bibliogr. 17 poz., rys., wykr.

Twórcy

autor

Hati S.

• Multimedia University, Cyberjaya 63100, Malaysia,

Bibliografia

• [1] Fischler M. A. and Bolles R. C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communication ACM, 241(6): 381-395, 1981.
• [2] Beightler C. S., Phillips D. N., and Wilde D. J.: Foundations of Optimization. Prentice Hall of India, 1982.
• [3] Besl P. J. and Jain R. C.: Three-dirnens1onal object recognition. Artificial Intelligence, 17(1): 75 145, 1982.
• [4] Kevin Weiler: Edge-based data strucures for solid modelling in eurved-surface environments. IEEE Computer Graphics and Applications, pages 21-10, 1982.
• [5] Franklin W. R. and Akman V.: Reconstructing visible regions from visible segments. BIT, 26:430-441. 17(5): 534-638, 1986.
• [6] Haralick R. M., Joo H., Lee ., Zhuang X., Vaidya V., and Kim M. B.: Pose estimation from corresponding point data. IEEE Trans. on SMC, 19(6): 1426-14416, 1989.
• [7] Joseph S. C. Yuan.: A general photogrammetric method for determining position and orientation. IEEE Trans. on Robotics and Automation, 5(2): 129-141, 1989.
• [8] Suetens P., Fua P., and Hanson A.: Computational strategies for object recognition. ACM Computing Serveys, 24(1): 5-55, 1992.
• [9] Gonzalez R. C. and Woods R. E.: Digital image processing. Addison-Wesley Publishing Company, 1993.
• [10] Kang S. B. and Ikeuchi K.: The complex egi: A new representation for 3-d pose determination. IEEE Trans. on Pattern Analysis and Machine Intelligence, 15(7): 707-721, 1993.
• [11] Xinhua Zhuang and Yan Huang: Robust 3d-3d pose estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence, 16(8): 813-824, August, 1994.
• [12] Abidi M. A. and Chandra T.: A new eflicient and direct solution for pose estimation using quadrangular targets: algorithm and evaluation. IEEE Trans. on Pattern Analysis and Machine Intelligence, IEEE Trans. PAMI, 24 (7): 932-946, 1995.
• [13] Newman T. S. and. Jain A. K.: A survey of automated visual inspection. Computer Vision and Image Understanding, 61(2): 231-262, March, 1995.
• [14] Tarel Jean-Philippe, Civi Haken, and Cooper David B.: Pose estimation of free-form 3d objects without point matching using algebraic surface models. In IEEE Workshop on Model Based 3D Image Analysis, 1998.
• [15] Heikkila Janne : Geometric camera calibration using circular control points. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10): 1066-1077, 2000.
• [16] Drummond Tom and Cipolla Roberto: Real-time visual tracking of complex structures. Abstract. This paper presents, 2002.
• [17] Grest Daniel, Herzog Dennis, and Koch Reinhard: Monocular body pose estimation by color histograms and point tracking. In Proc. of DAGM 2006, Berlin, Germany, September, 2006.