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Proceedings Paper

Development of two vision estimation algorithms for robot vision control scheme
Author(s): Jae Kyung Son; Wan Shik Jang; Yoon Gyung Sung; Yong Jun Yang
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Paper Abstract

This study deals with the development of two vision estimation algorithms for robot vision control scheme. One is the Extended Kalman Filtering algorithm, and the other is the Newton-Raphson algorithm. The Newton-Raphson (N-R) algorithm consists of vision system model, camera parameters estimation scheme and joint angle estimation scheme. The Extended Kalman Filtering (EKF) algorithm consists of vision system model, process model and measurement model. In addition, the process and the measurement models include the camera parameters estimation scheme and the joint angle estimation scheme, respectively. The vision system model includes six camera internal and external parameters. Each algorithm has its strengths and weaknesses. The Newton-Raphson algorithm is based on iterations and can concurrently handle large amounts of data. On the other hand, it takes a lot of processing time and accordingly is not easy use for real-time robot control. The Extended Kalman Filtering algorithm is based on recursion and thus is faster, but it requires very accurate selection of initial values. In this study we use Monte-Carlo method for estimating initial values. Finally, the results of both algorithms are compared experimentally by tracking the moving target.

Paper Details

Date Published: 12 April 2012
PDF: 10 pages
Proc. SPIE 8409, Third International Conference on Smart Materials and Nanotechnology in Engineering, 84092D (12 April 2012); doi: 10.1117/12.915764
Show Author Affiliations
Jae Kyung Son, Chosun Univ. (Korea, Republic of)
Wan Shik Jang, Chosun Univ. (Korea, Republic of)
Yoon Gyung Sung, Chosun Univ. (Korea, Republic of)
Yong Jun Yang, Dongkang College (Korea, Republic of)


Published in SPIE Proceedings Vol. 8409:
Third International Conference on Smart Materials and Nanotechnology in Engineering
Jinsong Leng; Yoseph Bar-Cohen; In Lee; Jian Lu, Editor(s)

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