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ARS Home » Northeast Area » Kearneysville, West Virginia » Appalachian Fruit Research Laboratory » Innovative Fruit Production, Improvement, and Protection » Research » Publications at this Location » Publication #313873

Title: Parameterizations for reducing camera reprojection error for robot-world hand-eye calibration

Author
item Tabb, Amy
item AHMAD YOUSEF, KHALIL - The Hashemite University

Submitted to: IEEE RSJ International Conference on Intelligent Robots and Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/1/2015
Publication Date: 9/27/2015
Citation: Tabb, A., Ahmad Yousef, K.M. 2015. Parameterizations for reducing camera reprojection error for robot-world hand-eye calibration. IEEE RSJ International Conference on Intelligent Robots and Systems. p. 3030-3037.

Interpretive Summary: Accurate calibration of cameras and robots is needed to accomplish orchard automation tasks. Poor quality calibrations result in unpredictable results. In this paper, we offer two approaches to the robot-world, hand-eye calibration problem by using Euler angles for rotation parameterizations. These parameterizations result in lower camera reprojection errors than other state-of-the-art methods.

Technical Abstract: Accurate robot-world, hand-eye calibration is crucial to automation tasks. In this paper, we discuss the robot-world, hand-eye calibration problem which has been modeled as the linear relationship AX equals ZB, where X and Z are the unknown calibration matrices composed of rotation and translation components. While there are many different approaches to determining X and Z, including linear and iterative methods, we parameterize the rotation components using Euler angles and find a solution using an iterative method. We also offer a method to determine A, X, and Z, by formulating the robot-world, hand-eye calibration problem in terms of camera reprojection error. We compare both of these approaches to the state-of-the-art and conclude that our approaches yield lower values of camera reprojection error. In addition, we demonstrate the improved reconstruction accuracy then using the robot-world, hand-eye calibrations produced from our methods.