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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 #324969

Research Project: Integrated Orchard Management and Automation for Deciduous Tree Fruit Crops

Location: Innovative Fruit Production, Improvement, and Protection

Title: Solving the robot-world, hand-eye(s) calibration problem with iterative methods

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

Submitted to: Machine Vision and Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2017
Publication Date: 5/2/2017
Citation: Tabb, A., Ahmad Yousef, K.M. 2017. Solving the robot-world, hand-eye(s) calibration problem with iterative methods. Machine Vision and Applications. DOI: 10.1007/s00138-017-0841-7.

Interpretive Summary: Accurate calibration of cameras and robots is needed to accomplish automation tasks with robots. In this paper, we explore the camera-robot calibration problem using new formulations and methods of solving the problem, and are able to deal with calibrating more than one camera to a robot. We also compare our solutions to the existing methods in the literature. We conclude that our methods perform better than the existing algorithms on several metrics.

Technical Abstract: Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as AX = ZB, where X and Z are unknown homogeneous transformation matrices. The successful execution of many robot manipulation tasks depends on determining these matrices accurately, and we are particularly interested in the use of calibration for use in vision tasks. In this work, we describe a collection of methods consisting of two cost function classes, three different parameterizations of rotation components, and separable versus simultaneous formulations. We explore the behavior of this collection of methods on real datasets and compare to seven other state-of-the-art methods. Our collection of methods return greater accuracy on many metrics as compared to the state-of-the-art. The collection of methods is extended to the problem of robot-world hand-multiple eye calibration, and results are shown with two and three cameras mounted on the same robot.