The On-Line Calibration Research of the Picking Robot Binocular Vision System

Ke Yin Chen; Xiang Jun Zou; Li Juan Chen

doi:10.4028/www.scientific.net/KEM.522.634

Paper Titles

Experimental Study on Critical Vibration for the Load of Motion Platform of Linear Servo Motor
p.613

Tracking Control of Mobile Robots Based on the BP Neural Network
p.618

The Tool Space Trajectory and Stress Simulation of Manipulator Based on the Optimization Design
p.623

Automatic Inspection System of Small Plastic Gears Based on Machine Vision
p.628

The On-Line Calibration Research of the Picking Robot Binocular Vision System
p.634

Motion Path Control of Multi-DOF Handling Manipulator in Industrial Field Based on Vision and C-Space Obstacle Avoidance
p.638

Study on Method for External Characteristics Extraction and Classification of Tomatoes Used for Grading Robot
p.643

Application of the Two-Dimensional Movement and One-Dimensional Rotation 3 DOF Robot on Riveting System
p.649

Design of Remote Monitoring System of Robot Based on FPGA
p.654

HomeKey Engineering MaterialsKey Engineering Materials Vol. 522The On-Line Calibration Research of the Picking...

The On-Line Calibration Research of the Picking Robot Binocular Vision System

Abstract:

In the picking robot binocular vision systems research, the camera calibration is often an indispensable step and these basements to locate the target of the object and rebuild the three-dimensional construction based on the robot stereo vision for the follow-up study. So, searching for a high accuracy and simple camera calibration algorithm is of great significance and necessary. However, For most of these camera calibration algorithms, it is necessary to establish a reference object, namely the target, in front of the camera at present, but posing the target is very not convenient or almost impossible in some cases. Therefore, a picking robot online calibration algorithm based on the vision scene was proposed by studying the work environment characteristics of the picking robot binocular vision system and the invariant projective geometry. The experimental results showed that this algorithm’s calibration accuracy and precision good meets to the requirement of the robot binocular vision system camera calibration in the complex environment.

You might also be interested in these eBooks

Progress in Advanced Manufacturing Technologies

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 522)

Pages:

634-637

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.522.634

Citation:

Cite this paper

Online since:

August 2012

Authors:

Ke Yin Chen, Xiang Jun Zou, Li Juan Chen

Keywords:

Camera Calibration, Hough Transform (HT), Image Segmentation, Stereo Vision

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J.F. Reid, Q. Zhang, N. Noguehi and M. Diekson: Agrieultural Automatie Gguidanee Research in North America, Computelectronagr Vol. 25 (2000) No. 1, pp.155-167.

Google Scholar

[2] X. Zhao: China Agricultural Science Bulletin, Vol. 26(2010), pp.360-364.

Google Scholar

[3] P. Zhao, D. Zhao: Agricultural Research, Vol. 9(2009), pp.212-214.

Google Scholar

[4] Q. Zhong: Based on machine vision technology for target location of tomato harvesting robot (MS., Jiangsu University, China 2005). pp.58-62.

Google Scholar

[5] F. Han: Blue Berry Machine vision recognition and calibration technology research (Ph.D., Northeast Forestry University, China 2011). pp.47-61.

Google Scholar

[6] J.T. Xiong, X.J. Zou, L.J. Chen and A.X. Guo: Journal of Agricultural Machinery, Vol. 42 (2011), pp.162-166.

Google Scholar

[7] W.P. Zhu: Clustering algorithm based on image segmentation (MS., Jiangnan University, China 2009). pp.15-36.

Google Scholar

[8] H.B. Liu, J. Shen et al: Visual C + + digital image processing technology explanation (Mechanical Industry Press, China 2010).

Google Scholar