Abstract
We propose a novel approach to estimate the three degrees of freedom (DoF) drift-free rotational motion of an
RGB-D camera from only a single line and plane in the
Manhattan world (MW). Previous approaches exploit the
surface normal vectors and vanishing points to achieve accurate 3-DoF rotation estimation. However, they require
multiple orthogonal planes or many consistent lines to be
visible throughout the entire rotation estimation process;
otherwise, these approaches fail. To overcome these limitations, we present a new method that estimates absolute camera orientation from only a single line and a single plane
in RANSAC, which corresponds to the theoretical minimal
sampling for 3-DoF rotation estimation. Once we find an
initial rotation estimate, we refine the camera orientation by
minimizing the average orthogonal distance from the endpoints of the lines parallel to the MW axes. We demonstrate
the effectiveness of the proposed algorithm through an extensive evaluation on a variety of RGB-D datasets and compare with other state-of-the-art methods