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Data Selection for Hand-eye Calibration: A Vector Quantization Approach

Centre for Artificial Intelligence Research Auckland University of Technology Auckland, New Zealand, jochen.schmidt{at}aut.ac.nz

Heinrich Niemann

Lehrstuhl für Mustererkennung Universität Erlangen-Nürnberg 91058 Erlangen, Germany

This paper presents new vector quantization based methods for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements. Data selection can improve the accuracy of classic hand-eye calibration, and make it possible in the first place in situations where the standard approach of manually selecting positions is inconvenient or even impossible, especially when using continuously recorded data. A variety of methods is proposed, which differ from each other in the dimensionality of the vector quantization compared to the degrees of freedom of the rotation representation, and how the rotation angle is incorporated. The performance of the proposed vector quantization based data selection methods is evaluated using data obtained from a manually moved optical tracking system (hand) and an endoscopic camera (eye).

Key Words: hand-eye calibration • vector quantization

The International Journal of Robotics Research, Vol. 27, No. 9, 1027-1053 (2008)
DOI: 10.1177/0278364908095172


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