回転と並進の分離による3点線画像からの高精度リアルタイム相対姿勢推定
An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation
点と線の2D観測のみから3視点の相対運動を高精度かつリアルタイムに解くため、回転と並進を分離して推定する新しい姿勢ソルバを提案した。
著者: Zewen Xu, Yijia He, Hao Wei, Bo Xu, BinJian Xie, Yihong Wu
分類: cs.RO, cs.CV
原文アブストラクト
Line features are valid complements for point features in man-made environments. 3D-2D constraints provided by line features have been widely used in Visual Odometry (VO) and Structure-from-Motion (SfM) systems. However, how to accurately solve three-view relative motion only with 2D observations of points and lines in real time has not been fully explored. In this paper, we propose a novel three-view pose solver based on rotation-translation decoupled estimation. First, a high-precision rotation estimation method based on normal vector coplanarity constraints that consider the uncertainty of observations is proposed, which can be solved by Levenberg-Marquardt (LM) algorithm efficiently. Second, a robust linear translation constraint that minimizes the degree of the rotation components and feature observation components in equations is elaborately designed for estimating translations accurately. Experiments on synthetic data and real-world data show that the proposed approach improves both rotation and translation accuracy compared to the classical trifocal-tensor-based method and the state-of-the-art two-view algorithm in outdoor and indoor environments.
