How to efficiently and accurately handle image matching outliers is a
critical issue in two-view relative estimation. The prevailing RANSAC method
necessitates that the minimal point pairs be inliers. This paper introduces a
linear relative pose estimation algorithm for n $( n \geq 6$) point pairs,
which is founded on the recent pose-only imaging geometry to filter out
outliers by proper reweighting. The proposed algorithm is able to handle planar
degenerate scenes, and enhance robustness and accuracy in the presence of a
substantial ratio of outliers. Specifically, we embed the linear global
translation (LiGT) constraint into the strategies of iteratively reweighted
least-squares (IRLS) and RANSAC so as to realize robust outlier removal.
Simulations and real tests of the Strecha dataset show that the proposed
algorithm achieves relative rotation accuracy improvement of 2 $\sim$ 10 times
in face of as large as 80% outliers.

在两视相对估计中，本文介绍了一种基于最近的仅位姿成像几何来通过适当的重新加权过滤异常值的线性相对姿态估计算法，该算法能够处理平面退化场景，在存在高比例异常值的情况下提高鲁棒性和准确性，通过将线性全局平移约束嵌入迭代重新加权最小二乘 (IRLS) 和 RANSAC 的策略中来实现鲁棒异常值去除，Strecha 数据集的仿真和实际测试表明，该算法在面对高达 80% 的异常值时实现了 2 到 10 倍的相对旋转准确性改进。