Fast and globally convergent pose estimation from video images
Fast and globally convergent pose estimation from video images
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DOI：
10.1109/34.862199
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发表时间：
2000-06-01
影响因子：
23.6
通讯作者：
Mjolsness, E
中科院分区：
文献类型：
--
作者：
Lu, CP;Hager, GD;Mjolsness, E
确定将二维图像与已知的三维几何形状相关联的刚性变换是摄影测量和计算机视觉中的经典问题。因此，用于解决该问题的最佳方法依赖于迭代优化方法，该方法不能被证明收敛和/或不能有效地考虑旋转矩阵的正交结构。我们表明，姿态估计问题可以制定为最小化的误差度量的基础上，在对象（而不是图像）空间中的共线性。利用物空间共线误差，导出了一种直接计算正交旋转矩阵的全局收敛迭代算法。实验上，我们表明，该方法是计算效率高，ii是不低于目前最好的优化方法的准确性。并且它在对离群值的鲁棒性方面优于所有测试方法。
Determining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not effectively account for the orthonormal structure of rotation matrices. We show that the pose estimation problem can be formulated as that of minimizing an error metric based on collinearity in object (as opposed to image) space. Using object space collinearity error, we derive an iterative algorithm which directly computes orthogonal rotation matrices and which is globally convergent. Experimentally, we show that the method is computationally efficient, that ii is no less accurate than the best currently employed optimization methods. and that it outperforms all tested methods in robustness to outliers.