Abstract
As the robot makes unplanned movement, online hand-eye calibration determines the relative pose between the robot gripper/end-effector and the sensors mounted on it. With noisy measurements, hand-eye calibration is sensitive to small rotations in real applications. Moreover, degenerate cases such as pure translations have no effect in hand-eye calibration. This paper proposes an adaptive motion selection algorithm for online hand-eye calibration, which can adaptively set the thresholds of motion selection according to the characteristics of the unplanned motion sequence. It is achieved by using polynomial-regression to predict the relationship between RMS of calibration error and thresholds. Thus, this procedure leads to an adaptive method of motion selection. It can adapt itself to online hand-eye calibration in various applications. Experiments using simulated data are conducted and present good results. Experiments using real scenes also show that the method is promising.
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Zhang, J., Shi, F., Liu, Y. (2005). An Adaptive Selection of Motion for Online Hand-Eye Calibration. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_54
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DOI: https://doi.org/10.1007/11589990_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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