  Mobile AR applications benefit from fast initialization to display
world-locked effects instantly. However, standard visual odometry or SLAM
algorithms require motion parallax to initialize (see Figure 1) and, therefore,
suffer from delayed initialization. In this paper, we present a 6-DoF monocular
visual odometry that initializes instantly and without motion parallax. Our
main contribution is a pose estimator that decouples estimating the 5-DoF
relative rotation and translation direction from the 1-DoF translation
magnitude. While scale is not observable in a monocular vision-only setting, it
is still paramount to estimate a consistent scale over the whole trajectory
(even if not physically accurate) to avoid AR effects moving erroneously along
depth. In our approach, we leverage the fact that depth errors are not
perceivable to the user during rotation-only motion. However, as the user
starts translating the device, depth becomes perceivable and so does the
capability to estimate consistent scale. Our proposed algorithm naturally
transitions between these two modes. We perform extensive validations of our
contributions with both a publicly available dataset and synthetic data. We
show that the proposed pose estimator outperforms the classical approaches for
6-DoF pose estimation used in the literature in low-parallax configurations. We
release a dataset for the relative pose problem using real data to facilitate
the comparison with future solutions for the relative pose problem. Our
solution is either used as a full odometry or as a preSLAM component of any
supported SLAM system (ARKit, ARCore) in world-locked AR effects on platforms
such as Instagram and Facebook.
