Abstract
In a hand–eye collaborative measurement system, the measurement error caused by the position change of an industrial robot end can create challenges during the high-precision measurement of large-scale components. The relative instability between binocular stereo vision and robot end is discussed for the first time, and a relative instability compensation (RIC) method is proposed. Firstly, using a watershed-model based on the Gaussian mixture model clustering, and locking of the region of interest are achieved. The correction of ellipse distortion combined with camera internal parameters is employed, and the 3D coordinate is reconstructed. Subsequently, the common and link points are constructed to perceive the updating of the positions. Based on the assumption of small-angle deviation, a novel model is established for compensating the relative instability. Ultimately, the experiment results indicate that the proposed method is effective for improving the measurement precision, which is superior to 0.075 mm.
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