Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry
Qi Cai Xinrui Li Yuanxin Wu
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.