  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.
