Pose Estimation for 3D Work Piece Using Differential Evolution Algorithm

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In industrial fields, precise pose of a 3D object is the prerequisite of the subsequent tasks like grasping and assembly, thus many researches on accurate pose estimation of a 3D object are explored over the last decades. To get the pose of a 3D work piece from the 2D image data is a challenging task in industrial applications. This paper proposes a fully automated pose estimation system which is capable to estimate the accurate model and pose of a 3D work piece that can well match the 2D image data. This is achieved by representing the above problem as an optimization problem aiming at finding the accurate model parameters and pose parameters of work piece by minimize the difference between the real 2D image and the hypothetical 2D image that produced through the given parameters from 3D image. Due to the coupling of the unknown model and pose parameters and the discontinuity of the objective function, the above optimization problem cannot be solved through traditional optimization approaches. Hence, we utilize a heuristic optimization strategy - Differential Evolution to cope with the problem. The experimental results demonstrate the effectiveness of the proposed method.

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1708-1712

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June 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] D. G. Lowe: Fitting Parameterized Three-Dimensional Models to Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13 (1991), p.441–450.

DOI: 10.1109/34.134043

Google Scholar

[2] M. Dhome, M. Richetin, J.T. Lapresté and G. Rives: Determination of the Attitude of 3D Objects from a Single Perspective View. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11 (1989), pp.1265-1278.

DOI: 10.1109/34.41365

Google Scholar

[3] A. Hill, A. Thornham and C. J. Taylor: Model-based Interpretation of 3D Medical Images. Proceedings 4th British Machine Vision Conference, (1993), p.339–348.

DOI: 10.5244/c.7.34

Google Scholar

[4] K. Fong and S. Y. Yuen: A Genetic Algorithm with Coverage for Object Localization. Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Proceeding, (2001), pp.48-51.

DOI: 10.1109/isimp.2001.925327

Google Scholar

[5] X. Q. Zhang, W. M. Hu, M. Steve, X. Li and M. L. Zhu: Sequential Particle Swarm Optimization for Visual Tracking. IEEE International Conference on Computer Vision and Pattern Recognition (2008), pp.1-8.

DOI: 10.1109/cvpr.2008.4587512

Google Scholar

[6] R. Storn and K. V. Price: Differential evolution- A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, Vol. 11 (1997), pp.341-359.

DOI: 10.1023/a:1008202821328

Google Scholar

[7] R. A. Brooks: Symbolic Reasoning among 3D models and 2D images. Artificial Intelligence, Vol. 17 (1981), pp.285-348.

DOI: 10.1016/0004-3702(81)90028-x

Google Scholar

[8] J. Ponce, D. Chelberg and W.B. Mann: Invariant Properties of Straight Homogeneous Generalized Cylinders and Their Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11 (1989), pp.951-966.

DOI: 10.1109/34.35498

Google Scholar

[9] G. Borgefos: Hierarchical Chamfer Matching: A Parametric Edge-matching Algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 10 (1998), pp.849-865.

DOI: 10.1109/34.9107

Google Scholar