摘要
A new calibration algorithm for multi-camera systems using 1D calibration objects is proposed. The algorithm inte- grates the rank-4 factorization with Zhang (2004)'s method. The intrinsic parameters as well as the extrinsic parameters are re- covered by capturing with cameras the 1D object's rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depth of which is estimated in an analytical equation instead of a recursive form. For more than three points on a 1D object, the approach of our algorithm is to extend the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. Simulations and experiments with real images verify that the proposed technique achieves a good trade-off between the intrinsic and extrinsic camera parameters.
A new calibration algorithm for multi-camera systems using 1D calibration objects is proposed. The algorithm integrates the rank-4 factorization with Zhang （2004）＇s method. The intrinsic parameters as well as the extrinsic parameters are recovered by capturing with cameras the 1D object＇s rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depth of which is estimated in an analytical equation instead ofa recursive form. For more than three points on a 1D object, the approach of our algorithm is to extend the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. Simulations and experiments with real images verify that the proposed technique achieves a good trade-off between the intrinsic and extrinsic camera parameters.
基金
the National Natural Science Foundation of China (No. 60675017)
the National Basic Research Program of China (No. 2006CB303103)
关键词
因素分解
测量矩阵
投影
机器人技术
Multi-camera calibration, Homography, Factorization, Scaled measurement matrix, Projective depth
