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Proceedings Paper

Overview Of A Unified Calibration Trio For Robot Eye, Eye-To-Hand, And Hand Calibration Using 3D Machine Vision
Author(s): Roger Y. Tsai; Reimar K. Lenz
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Paper Abstract

The machine vision community needs a simple, easy to use, fast, accurate, and low cost way of calibrating the robot eye, eye-to-hand and hand for 3D machine vision applications. This paper overviews a trio exactly for this purpose. The trio is real time, and uses common motion, cal-ibration plate, setup, coordinate systems, matrices, vectors, symbols and operations throughout. It is easier and faster than any of the existing techniques, and is among the most accurate calibration techniques using vision (while several orders of magnitude faster than those techniques that have comparable accuracy). The robot makes a series of automatically planned movements with a camera rigidly mounted at the gripper. At the end of each move, it takes a total of 90 milliseconds to grab an image, extract image feature coordinates and perform camera extrinsic calibration. After the robot finishes all the movements, it takes only a few milliseconds to do the eye-to-hand and hand calibration, and takes less than 25 milliseconds to do the eye calibration (can be reduced several fold if only a minimal number of calibration points are used). In this paper, we first introduce what a trio is. Then a list of main advantages of the proposed trio is given. Next, each element of the trio is defined. The common setup used throughout the trio is then described. Next, we briefly overview how the trio works globally. Then, each element of the trio is presented in more detail. The trio has been fully implemented and is operational; with the eye a Javelin CID camera, and the hand an IBM Clean Room Robot. This paper only overviews the approaches. The complete details of the theory, algorithm, and implementation results can be found in the references.

Paper Details

Date Published: 5 January 1989
PDF: 12 pages
Proc. SPIE 1003, Sensor Fusion: Spatial Reasoning and Scene Interpretation, (5 January 1989); doi: 10.1117/12.948932
Show Author Affiliations
Roger Y. Tsai, IBM T. J. Watson Research Center (United States)
Reimar K. Lenz, IBM T. J. Watson Research Center (United States)

Published in SPIE Proceedings Vol. 1003:
Sensor Fusion: Spatial Reasoning and Scene Interpretation
Paul S. Schenker, Editor(s)

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