JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Stereo Vision-Based 3D Pose Estimation of Product Labels for Bin Picking
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Stereo Vision-Based 3D Pose Estimation of Product Labels for Bin Picking
Udaya, Wijenayake; Choi, Sung-In; Park, Soon-Yong;
 
 Abstract
In the field of computer vision and robotics, bin picking is an important application area in which object pose estimation is necessary. Different approaches, such as 2D feature tracking and 3D surface reconstruction, have been introduced to estimate the object pose accurately. We propose a new approach where we can use both 2D image features and 3D surface information to identify the target object and estimate its pose accurately. First, we introduce a label detection technique using Maximally Stable Extremal Regions (MSERs) where the label detection results are used to identify the target objects separately. Then, the 2D image features on the detected label areas are utilized to generate 3D surface information. Finally, we calculate the 3D position and the orientation of the target objects using the information of the 3D surface.
 Keywords
bin picking;stereo vision;MSER;pose estimation;
 Language
English
 Cited by
1.
3차원 비전 기술을 이용한 라벨부착 소형 물체의 정밀 자세 측정,김응수;김계경;;박순용;

제어로봇시스템학회논문지, 2016. vol.22. 10, pp.839-846 crossref(new window)
 References
1.
K. Rahardja and A. Kosaka, "Vision-based bin-picking: recognition and localization of multiple complex objects using simple visual cues," Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 1448-1457, 1996.

2.
F. Boughorbel, Y. Zhang, S. Kang, U. Chidambaram, B. Abidi, A. Koschan, and M. Abidi, "Laser ranging and video imaging for bin picking," Assembly Automation, vol. 23, no. 1, pp. 53-59, 2003. crossref(new window)

3.
M. Berger, G. Bachler, and S. Scherer, "Vision guided bin picking and mounting in a flexible assembly cell," Intelligent Problem Solving. Methodologies and Approaches, vol. 1821, R. Logananthara, G. Palm, and M. Ali, Eds. Springer Berlin Heidelberg, pp. 109-117, 2000.

4.
I. K. Park, M. Germann, M. D. Breitenstein, and H. Pfister, "Fast and automatic object pose estimation for range images on the GPU," Machine Vision and Applications, vol. 21, no. 5, pp. 749-766, 2010. crossref(new window)

5.
J. Kirkegaard and T. B. Moeslund, "Bin-picking based on harmonic shape contexts and graph-based matching," Proc. of International Conference on Pattern Recognition, vol. 2, pp. 581-584, 2006.

6.
W. Y. Chiu, "Dual laser 3D scanner for random bin picking system," Proc. of International Conference on Advanced Robotics and Intelligent Systems (ARIS), pp. 1-3, 2015.

7.
D. Buchholz, M. Futterlieb, S. Winkelbach, and F. M. Wahl, "Efficient bin-picking and grasp planning based on depth data," Proc. of IEEE International Conference on Robotics and Automation, pp. 3245-3250, 2013.

8.
J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust wide-baseline stereo from maximally stable extremal regions," Image and Vision Computing, vol. 22, no. 10, pp. 761-767, Sep. 2004. crossref(new window)

9.
M. Donoser and H. Bischof, "Efficient maximally stable extremal region (MSER) tracking," Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2006.

10.
D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. crossref(new window)

11.
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge Univ Press, 2003.

12.
Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analisys and Machine Intelligence, vol. 22, no. 11, pp. 1330-1334, 2000. crossref(new window)