On vision systems identification with application to fixed camera robotic systems

Vision system calibration and identification are important issues for effec tive implementation of high-performance robotic systems. Vision system iden tification addresses the problem of determining the mapping from points in the world frame to their corresponding location in a computer image frame. By assuming rotation of the camera frame around one of the principal axes o f the world frame-but incorporating radial lens distortion-we show that thi s mapping can be expressed as a linear regression model in terms of a suita ble combination of the intrinsic and extrinsic camera parameters. This prop erty allows the application of several known techniques based on resolution of a determined set of linear equations and least-squares-based methods to estimate these parameters from experimental input-output data. Experimenta l comparisons are carried out to illustrate the performances of these metho ds. (C) 2001 John Wiley & Sons. Inc.