Freely expandable visual measurement network for large scale space based on auto calibration method

Abstract

Large-scale space measurement is of great significance for the assembly of large parts. In view of the problems of traditional measurement methods, such as difficult expansion of the measurement range, complicated calibration process, and poor portability, this work proposes a multicamera vision measurement network that can freely expand the measurement range based on the auto calibration method. First, cameras were fixed to auxiliary targets and every camera with a target was mounted on a precision turntable. Second, the rotation axis parameters of the camera and the target pose relative to the camera coordinate system were estimated. Finally, the extrinsic parameters between the cameras could be estimated by interactive scanning method. The experimental results show that the method in this paper simplifies the calibration method of the multicamera vision measurement network, and the measurement accuracy basically reaches the accuracy of the Zhang type calibration method under the premise that the measurement range can be expanded freely.