关于相机自标定算法的研究

Research on camera self-calibration algorithms

针对传统相机自标定方法需要利用三个正交方向的消失点,在实际场景中不容易满足并且无法求得畸变系数的问题,提出了一种改进的相机标定的方法,该方法基于二消失点和低秩纹理对车载相机进行标定和畸变矫正。首先引用RANSAC算法进行消失点查找,利用相机成像的投影模型和消失点之间的几何特性求解相机的焦距和外参;再利用图像的低秩性进行建模,通过增广拉格朗日算法对模型进行迭代求出相机的畸变系数。实验结果表明该方法具备较好的精度和实用性。

In response to the challenges posed by traditional self-calibration methods for cameras,which require three orthogonal vanishing points that are often difficult to satisfy in practical scenarios,and face difficulties in determining distortion coefficients,this paper proposes an improved camera calibration method.The proposed method introduces a technique based on two vanishing points and low-rank textures for calibrating and correcting distortion in an onboard camera.The method initially employs the RANSAC algorithm for vanishing point detection,utilizing the geometric properties between the vanishing points and the camera imaging projection model to solve for the camera's focal length and extrinsic parameters.Then it models the image with low-rank characteristics and iteratively solves for the camera's distortion coefficients using an augmented Lagrangian algorithm.Experimental results demonstrate that the proposed method exhibits superior accuracy and practicality.