带约束的摄像机定标方法

Camera Tracking with Constraint

由于积累误差和摄像机内部参数的校正误差,基于视觉的摄像机定标结果尽管在图像上的重投影误差很小,但是重建的空间结构往往存在一定程度的扭曲。提出了一种带约束的摄像机定标算法,将场景中存在几何条件(如摄像机路径在一条直线上)作为约束条件,对摄像机定标结果进行再优化,使得重建的空间与真实世界的欧氏空间更为一致。在优化过程中,将欧氏空间与图像空间的两种误差约束在同一范围,自动获取最佳的约束系数。实验结果证明了该算法的有效性。

Structure reconstructed from motion is usually distorted due to accumulated error and calibration error from extrinsic parameters of video cameras, although the re-projected error sometimes can be small. This paper proposes a novel approach to structure from motion with the geometry conditions existing in the scene, such as the trajectory of a video camera lying on a straight line. With this new method, distortion of the reconstructed results can be removed. We also propose a method to automatically select the optimal coefficients between the original cost and the constraint cost, through which the best reconstruction is obtained. The implemented examples demonstrate very precise structure and motion recovery, which prove the effectiveness and robustness of the proposed method.