A New Camera Calibration Algorithm Based on Adaptive Image Selection

Camera calibration plays an important role in the 3D reconstruction task. However, in the calibration process, users need to select some key images from a large number of calibration board images for further performing the maximum likelihood estimation of camera model parameters. Due to the subjectivity of this estimation, it is difficult to guarantee the consistency of the results obtained by different testers. In this paper, a new camera calibration image selection algorithm is proposed to obtain high accuracy intrinsic parameters. Users only need to acquire a series of checkerboard image sequences, randomly select one image from the image sequence each time, and determine whether the image can be used for camera calibration by calculating the angle error of single frame checkerboard corner. This method can adaptively select a small number of images from the image sequence for camera calibration. The experimental results show that this self-built calibration algorithm is not only simple in the operation process, but also has higher accuracy and consistency in calibration results when compared with traditional calibration method.