Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry

• URL: http://arxiv.org/abs/2401.13357v1
• Date: Wed, 24 Jan 2024 10:35:34 GMT
• Title: Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry
• Authors: Qi Cai, Xinrui Li, Yuanxin Wu
• Abstract summary: This paper introduces a linear relative pose estimation algorithm for n $( n geq 6$) point pairs. It is founded on the recent pose-only imaging geometry to filter out outliers by proper reweighting. It achieves relative rotation accuracy improvement of 2 $sim$ 10 times in face of as large as 80% outliers.
• Score: 18.270330814061325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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