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.

翻译：如何高效且准确地处理图像匹配外点是两视图相对估计中的关键问题。主流RANSAC方法要求最小点对为内点。本文提出一种针对n（$ n \geq 6$）组点对的线性相对位姿估计算法，该算法基于近期提出的纯位姿成像几何，通过恰当加权来滤除外点。所提算法能够处理平面退化场景，并在存在大量外点情况下提升鲁棒性与精度。具体而言，我们将线性全局平移（LiGT）约束嵌入迭代重加权最小二乘（IRLS）和RANSAC策略中，以实现鲁棒的外点剔除。在Strecha数据集上的仿真与实测表明，当外点比例高达80%时，所提算法的相对旋转精度可提升2~10倍。