摘要
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 result. The improved method has a 50% improvement of re-projection accuracy compared with our previous method. The measurement accuracy is maintained well within 1.5% of the overall measurement depth range.
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 result. The improved method has a 50% improvement of re-projection accuracy compared with our previous method. The measurement accuracy is maintained well within 1.5% of the overall measurement depth range.
基金
supported partially by the National"863"Program of China(No.2005AA420240)
the Doctoral Foundation of the Ministry of Education of China(No.20070287055)
