An Object Detection and Pose Estimation Approach for Position Based Visual Servoing
DOI:
https://doi.org/10.1515/ecce-2017-0005Keywords:
Object recognition, Pose estimation, Stereo vision, Visual servoingAbstract
In this paper, an object recognition method and a pose estimation approach using stereo vision is presented. The proposed approach was used for position based visual servoing of a 6 DoF manipulator. The object detection and recognition method was designed with the purpose of increasing robustness. A RGB color-based object descriptor and an online correction method is proposed for object detection and recognition. Pose was estimated by using the depth information derived from stereo vision camera and an SVD based method. Transformation between the desired pose and object pose was calculated and later used for position based visual servoing. Experiments were carried out to verify the proposed approach for object recognition. The stereo camera was also tested to see whether the depth accuracy is adequate. The proposed object recognition method is invariant to scale, orientation and lighting condition which increases the level of robustness. The accuracy of stereo vision camera can reach 1 mm. The accuracy is adequate for tasks such as grasping and manipulation.References
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E. Malis, F. Chaumette, and S. Boudet, “2 1/2 D visual servoing,” IEEE Transactions on Robotics and Automation, vol. 15, no. 2, pp. 238–250, Apr. 1999. https://doi.org/10.1109/70.760345
C. Peng and J Krumm, “Object recognition with color cooccurrence histograms,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999. vol. 2, 1999. https://doi.org/10.1109/cvpr.1999.784727
C. Ancuti and P. Bekaert, “SIFT-CCH: Increasing the SIFT distinctness by Color Co-occurrence Histograms,” 2007 5th International Symposium on Image and Signal Processing and Analysis, Sep. 2007. https://doi.org/10.1109/ispa.2007.4383677
K. E. A. van de Sande, T. Gevers, and C. G. M. Snoek, “Evaluating Color Descriptors for Object and Scene Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1582–1596, Sep. 2010. https://doi.org/10.1109/tpami.2009.154
D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004. https://doi.org/10.1023/b:visi.0000029664.99615.94
H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, Jun. 2008. https://doi.org/10.1016/j.cviu.2007.09.014
S. Leutenegger, M. Chli, and R. Y. Siegwart, “BRISK: Binary Robust invariant scalable keypoints,” 2011 International Conference on Computer Vision, Nov. 2011. https://doi.org/10.1109/iccv.2011.6126542
O. Wasenmüller and D. Stricker, “Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision,” Lecture Notes in Computer Science, pp. 34–45, 2017. https://doi.org/10.1007/978-3-319-54427-4_3
W. Kabsch, “A solution for the best rotation to relate two sets of vectors,” Acta Crystallographica Section A, vol. 32, no. 5, pp. 922–923, Sep. 1976. https://doi.org/10.1107/s0567739476001873
W. Kabsch, “A discussion of the solution for the best rotation to relate two sets of vectors,” Acta Crystallographica Section A, vol. 34, no. 5, pp. 827–828, Sep. 1978. https://doi.org/10.1107/s0567739478001680
P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239–256, Feb. 1992. https://doi.org/10.1109/34.121791
K. S. Arun, T. S. Huang, and S. D. Blostein, “Least-Squares Fitting of Two 3-D Point Sets,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, no. 5, pp. 698–700, Sep. 1987. https://doi.org/10.1109/tpami.1987.4767965
J. Heikkila, “Geometric camera calibration using circular control points,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1066–1077, 2000. https://doi.org/10.1109/34.879788
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2017-07-01
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Shi, L. (2017). An Object Detection and Pose Estimation Approach for Position Based Visual Servoing. Electrical, Control and Communication Engineering, 12(1), 34-39. https://doi.org/10.1515/ecce-2017-0005
