受约束的稀疏光束法平差在相机标定中的应用

Camera Calibration Optimization with Constrained Sparse Bundle Adjustment

直接用稀疏的光束法平差(SBA)优化张正友单相机标定算法结果会得到多组不同的相机内部参数和畸变参数(统称相机参数)。本文在SBA数学模型的基础之上增加了相机参数相等的约束,建立了一种受约束的稀疏光束法平差(CSBA)模型,提出了一种新的矩阵分块策略,提高了稀疏线性方程组的求解效率。运用模拟实验,验证了CSBA算法在图像特征点像素坐标不具备零均值高斯误差时也能得到唯一的优化相机参数。最后将所提CSBA算法应用于双目立体视觉系统,实测实验结果表明,所提算法能够同时优化立体视觉中的相机内外部参数并提高三维重建结果的精度。

If Zhang＇s camera calibration results are optimized with SBA directly, different sets of camera parameters （internal parameters and distortion parameters） will be obtained. Based on the mathematical model of SBA and the equality constraints of camera parameters, a Constrained Sparse Bundle Adjustment （CSBA） algorithm is proposed with a new block matrix partition strategy to improve the efficiency of solving sparse linear equations. Simulation experiments are implemented to verify that unified camera parameters can be obtained even if the pixel coordinates don＇t have zero-mean Gaussian error. Finally, the CSBA algorithm is applied to a binocular stereo vision system. The experimental results demonstrate that the CSBA algorithm can optimize the camera parameters and position parameters simultaneously, and improve the accuracy of 3D reconstruction.