Abstract:System calibration, which usually involves complicated and time-consuming procedures, is crucial for any three-dimensional (3D) shape measurement system based on vision. A novel improved method is proposed for accurate calibration of such a measurement system. The system accuracy is improved with considering the nonlinear measurement error created by the difference between the system model and real measurement environment. We use Levenberg-Marquardt optimization algorithm to compensate the error and get a good… Show more
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