Determining Extrinsic Parameters for Active Stereovision

Huawei Wang; De Xu

doi:10.20965/jaciii.2009.p0076

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JACIII Vol.13 No.2 pp. 76-79

doi: 10.20965/jaciii.2009.p0076

(2009)

Paper:

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Determining Extrinsic Parameters for Active Stereovision

Huawei Wang and De Xu

Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:

July 3, 2008

Accepted:

January 15, 2009

Published:

March 20, 2009

Keywords:

extrinsic parameters, active stereovision, calibration

Abstract

In the novel method we propose for determining extrinsic parameters for active stereovision, we first map the relationship between rotational and yaw angles based on least squares fitting, then optimize the rotational axis between two cameras using the Levenberg-Marquardt algorithm. Extrinsic parameters are then easily derived for active stereovision based on the mapping model without complex recalibration. The results of experiments confirmed our proposed method's feasibility.

Cite this article as:

H. Wang and D. Xu, “Determining Extrinsic Parameters for Active Stereovision,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.2, pp. 76-79, 2009.

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References

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[8] H. J. Kim, M. H. Yoo, and S. W. Lee, “A control model for vergence movement on a stereo robotic head using disparity flux,” Proc. of the 15th Int. Conf. on Pattern Recognition, Vol.4, pp. 491-494, 2000.
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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. Y. Tsai, “A versatile camera calibration technique for high accuracy 3D machine vision metrology using off the shelf TV cameras and lens,” IEEE Journal of Robotics Automation, Vol.3, No.4, pp. 323-344, 1987.

[2] [2] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.22, No.11, pp. 1330-1334, 2000.

[3] [3] S. Bougnoux, “From projective to Euclidean space under any practical situation, a criticism of self-calibration,” Proc. of the 6th Int. Conf. on Computer Vision, pp. 790-796, 1998.

[4] [4] Q. T. Luong and O. Faugeras, “Self-calibration of a moving camera from point correspondences and fundamental matrices,” Int. Journal of Computer Vision, Vol.22, No.3, pp. 261-289, 1997.

[5] [5] S. J. Maybank and O. Faugeras, “A theory of self-calibration of a moving camera,” Int. Journal of Computer Vision, Vol.8, No.2, pp. 123-151, 1992.

[6] [6] M. Pagel, E. Mael, and C. Malsburg, “Self-calibration of the fixation movement of a stereo camera,” Machine Learning, Vol.31, No.13, pp. 169-186, 1998.

[7] [7] H. Zou, Z. Gong, and J. Luo. “State of the art and trend of biomimetic eye,” Robot, Vol.27, No.5, pp. 469-474, 2005.

[8] [8] H. J. Kim, M. H. Yoo, and S. W. Lee, “A control model for vergence movement on a stereo robotic head using disparity flux,” Proc. of the 15th Int. Conf. on Pattern Recognition, Vol.4, pp. 491-494, 2000.

[9] [9] D. Xu, Y. F. Li, M. Tan, and Yang Shen, “A new active visual system for humanoid robots,” IEEE Transactions on System, Man & Cybernetics-Part B: Cybernetics, Vol.38, No.2, pp. 320-330, 2008.

[10] [10] R. M Haralick, D. Lee, K. Ottenburg, and M. Nolle, “Analysis and solutions of the three point perspective pose estimation problem,” Proc. of the Int. Conf. on Computer Vision and Pattern Recognition, pp. 592-598, 1991.

[11] [11] D. Dementhond, “Exact and approximate solutions of the perspective-3-point problem,” IEEE Transactions on pattern analysis and machine intelligence, Vol.14, No.11, pp. 1100-1105, 1992.