Inertial-Only Optimization for Visual-Inertial Initialization
This improves robustness and efficiency for visual-inertial SLAM systems, such as ORB-SLAM, by providing a faster and more accurate initialization method.
The authors tackled the problem of visual-inertial initialization by formulating it as a maximum-a-posteriori estimation problem to account for IMU measurement uncertainty, achieving initialization in less than 4 seconds with a 5.3% scale error on average on the EuRoC dataset.
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.