An optical imaging based fully automatic stereo vision system for aircraft assembly online measurement is presented and analyzed. In this system, the relative position of the two cameras can easily calibrated by imaging an optical reference bar in different locations and orientations throughout the measurement space according to epipolar constraint and the certified distance of the features on the reference bar. For measurement, the system takes infrared LEDs which attached at measured object as imaging targets, and makes use of the measurement results of these feature points space coordinates to calculate the related positions of the assembly aircraft parts. Furthermore, using automatic infrared LED light intensity and CCD cameras exposure time improve the calibration and measurement accuracy. The effectiveness of the proposed system has been test by experiments.

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As one of the key techniques of vision coordinates measurement, camera internal parameters calibration is regarded by technologists and has been researched a lot. Based on the study of regular internal parameters calibration methods such as plane calibration, two-plane calibration, vanishing points calibration, stereo calibration and so on, combined with the requirement of large space vision coordinates measurement for camera internal parameters calibration, this paper brings forward independent internal parameters calibration and virtual stereo calibration methods. A special calibration equipment is used in independent internal parameters calibration to calibrate original points coordinates of camera and distortion parameters of lens, and a virtual target board and bundle adjustment algorithm are used in virtual stereo calibration to calibrate internal parameters. They can be used in large space vision coordinates measurement and get high calibration accuracy.

The precision of target sub-pixel centroid location directly affects the result of large scale vision 3D coordinates measurement. This paper deeply studies the sub-pixel centroid location algorithm of retro-reflective targets and infrared optical targets used in 3D coordinates measurement system, and makes use of improved cubic convolution interpolation algorithm to increase the number of effective pixels used in centroid location, then gives optimizing adjustment parameters for different types of targets and combined with squared gray weighted centroid location algorithm, finally realizes accurate target sub-pixel centroid location. This algorithm is proved to be effective and robust by simulations and experiments. INTRUDUCTIONWith the development of close-range vision measurement technology, the large scale vision 3D coordinates measurement system is widely used in the machining of large workpieces, online measurement in the process of manufacture and assembly, prototype measurement of converse engineering, deformational measurement and analysis and so on [1][2][3][4] . Technologists all over the world have studied a lot on 3D coordinate vision measurement systems and corresponding algorithms to improve their measurement range and precision and satisfy the precision requirement of the manufacture and assembly of large pieces.As one of the key techniques of large scale vision coordinates measurement, the precision of imaging target centroid location directly influences the precision of 3D coordinates measurement [5,6] . And different solution algorithms have been brought forward for how to improve the precision of sub-pixel centroid location. For example, average of perimeter, binary centroid, gray scale centroid, ellipse fitting and Gaussian distribution fitting are all effective algorithms. But for the traditional location algorithms, because the original hardware is not changed, the number of effective pixels is not enough, and the sub-pixel centroid location precision cannot reach the requirement of accurate large scale vision 3D coordinates measurement.For this reason, according to the characteristics of circle retro-reflective targets and infrared optical targets used in large scale 3D coordinate measurement system, we bring forward target centroid location method using improved cubic convolution interpolation algorithm combined with gray weighted or squared gray weighted sub-pixel centroid location *

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