This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on $L_1$ norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.

通过推导摄像机拍摄同一场景点的新型线性位置约束，得出一种直接的全局摄像机平移估计线性方法，解决了共线摄像机运动和弱图像关联的问题。最终线性公式不涉及场景点坐标，使其适用于大规模数据，采用基于 $L_1$ 范数的线性方程求解，使该系统对基础矩阵和特征对应的离群值更加鲁棒，在顺序捕获图像和无序互联网图像上进行实验，证明了其在鲁棒性，精度和效率方面的优势。