Subset-based stereo calibration method optimizing triangulation accuracy

Calibration of vision systems is essential for performing measurement in real world coordinates. For stereo vision, one performs stereo calibration, the results of which are used for 3D reconstruction of points imaged in the two cameras. A common and flexible technique for such calibration is based on collection and processing pairs of images of a planar chessboard calibration pattern. The inherent weakness of this approach lies in its reliance on the random nature of data collection, which might lead to better or worse calibration results, depending on the collected set of image pairs. In this paper, a subset-based approach to camera and stereo calibration, along with its implementation based on OpenCV, is presented. It utilizes a series of calibration runs based on randomly chosen subsets from the global set of image pairs, with subsequent evaluation of metrics based on triangulating the features in each image pair. The proposed method is evaluated on a collected set of chessboard image pairs obtained with two identical industrial cameras. To highlight the capabilities of the method to select the best-performing calibration parameters, a principal component analysis and clustering of the transformed data was performed, based on the set of metric measurements per each calibration run.

Structured Light 3D Reconstruction System Based on a Stereo Calibration Plate

Calibration is a critical step in structured light 3D imaging systems. However, in the traditional calibration process, since the calibration plate is based on a two-dimensional model, the flatness of the calibration plate and the angle of the photo will affect the subsequent stitching steps based on the feature points. The number of photos also affects the calibration results. To improve the calibration accuracy, multiple photos need to be taken. The primary objective of this study was to achieve the simple and fast calibration of system parameters, so a method obtaining a large number of calibration data by homography matrix is presented, and a corresponding stereo target is designed in symmetry. First, using the relationship between the corner coordinates of the left and right parts of the stereo calibration plate and the coordinates of the world coordinate system, the homography matrix of the left and right calibration plates from the image coordinates to the world coordinates is calculated. Second, all the pixels in the stereo calibration plate are matched to the world coordinate system by using the homography matrix. In addition, we also compared the results of this method with those of traditional calibration methods. The experimental results show that the 3D geometric surface of the reconstruction result is smooth, it avoids the missing parts and the visual effect is excellent. Furthermore, the error range of small and complex objects can be reduced to 0.03 mm~0.05 mm. This method simplifies the calibration steps, reduces the calibration costs and has practical application value.

Calibration of a Stereo Radiation Detection Camera Using Planar Homography

This paper proposes a calibration technique of a stereo gamma detection camera. Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision society. However, few or no stereo calibration has been investigated in the radiation measurement research. Since no visual information can be obtained from a stereo radiation camera, it is impossible to use a general stereo calibration algorithm directly. In this paper, we develop a hybrid-type stereo system which is equipped with both radiation and vision cameras. To calibrate the stereo radiation cameras, stereo images of a calibration pattern captured from the vision cameras are transformed in the view of the radiation cameras. The homography transformation is calibrated based on the geometric relationship between visual and radiation camera coordinates. The accuracy of the stereo parameters of the radiation camera is analyzed by distance measurements to both visual light and gamma sources. The experimental results show that the measurement error is about 3%.