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Proceedings Paper

Image registration algorithm for high-voltage electric power live line working robot based on binocular vision
Author(s): Chengqi Li; Zhigang Ren; Bo Yang; Qinghao An; Xiangru Yu; Jinping Li
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Paper Abstract

In the process of dismounting and assembling the drop switch for the high-voltage electric power live line working (EPL2W) robot, one of the key problems is the precision of positioning for manipulators, gripper and the bolts used to fix drop switch. To solve it, we study the binocular vision system theory of the robot and the characteristic of dismounting and assembling drop switch. We propose a coarse-to-fine image registration algorithm based on image correlation, which can improve the positioning precision of manipulators and bolt significantly. The algorithm performs the following three steps: firstly, the target points are marked respectively in the right and left visions, and then the system judges whether the target point in right vision can satisfy the lowest registration accuracy by using the similarity of target points’ backgrounds in right and left visions, this is a typical coarse-to-fine strategy; secondly, the system calculates the epipolar line, and then the regional sequence existing matching points is generated according to neighborhood of epipolar line, the optimal matching image is confirmed by calculating the similarity between template image in left vision and the region in regional sequence according to correlation matching; finally, the precise coordinates of target points in right and left visions are calculated according to the optimal matching image. The experiment results indicate that the positioning accuracy of image coordinate is within 2 pixels, the positioning accuracy in the world coordinate system is within 3 mm, the positioning accuracy of binocular vision satisfies the requirement dismounting and assembling the drop switch.

Paper Details

Date Published: 19 December 2017
PDF: 10 pages
Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 106130K (19 December 2017); doi: 10.1117/12.2300502
Show Author Affiliations
Chengqi Li, State Grid Shandong Electric Power Research Institute (China)
Zhigang Ren, State Grid Shandong Electric Power Co. (China)
Bo Yang, State Grid Shandong Electric Power Co. (China)
Qinghao An, Univ. of Jinan (China)
Xiangru Yu, Univ. of Jinan (China)
Jinping Li, Univ. of Jinan (China)


Published in SPIE Proceedings Vol. 10613:
2017 International Conference on Robotics and Machine Vision
Chiharu Ishii; Genci Capi; Jianhong Zhou, Editor(s)

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