关键词: 摄像机自标定, 基础矩阵, Kruppa方程, 相对定向, 绝对定向, 光束法平差

Abstract: This paper proposed a new camera self-calibration method based on image sequences, which combined the theory of photogrammetry and computer vision. Firstly, the camera foundation matrix F was obtained by making use of the 8 points algorithm according to the image matching points, and the matrix C was got based on utilizing Kruppa equation and the foundation matrix F, then the matrix C was decomposed with Cholesky method to get the camera internal parameter matrix K. Secondly, the relative orientation and absolute orientation were carried on taking the internal parameters as the initial value, then the image matching points's 3d space coordinate was got by least squares forward intersection. Finally, the accurate camera internal parameters, external parameters and distortion coefficients were calculated by three-dimensional direct linear transformation and bundle adjustment methods. This self-calibration method does not depend on the particular geometric constraints in the scene, and it can be carried out as long as there are matching points, so it has broad applicability. Both simulation data and real images were used to test the method, and the results show that: this new method gets high precision and low computational complexity; it is a new and valid method for camera self-calibration.

Key words: camera self-calibration, foundation matrix, Kruppa equation, relative orientation, absolute orientation, bundle adjustment