  We formulate for the first time visual-inertial initialization as an optimal
estimation problem, in the sense of maximum-a-posteriori (MAP) estimation. This
allows us to properly take into account IMU measurement uncertainty, which was
neglected in previous methods that either solved sets of algebraic equations,
or minimized ad-hoc cost functions using least squares. Our exhaustive
initialization tests on EuRoC dataset show that our proposal largely
outperforms the best methods in the literature, being able to initialize in
less than 4 seconds in almost any point of the trajectory, with a scale error
of 5.3% on average. This initialization has been integrated into ORB-SLAM
Visual-Inertial boosting its robustness and efficiency while maintaining its
excellent accuracy.
