论文标题
仅惯性优化用于视觉惯性初始化
Inertial-Only Optimization for Visual-Inertial Initialization
论文作者
论文摘要
我们首次以最大A-posteriori（MAP）估计的意义将视觉惯性初始化作为最佳估计问题。这使我们能够正确考虑IMU测量不确定性，这在以前的方法中忽略了，该方法要么求解了代数方程组或使用最小二乘的临时成本函数最小化。我们在Euroc数据集上进行的详尽初始化测试表明，我们的提案在很大程度上优于文献中最佳方法，能够在轨迹的几乎任何点内在不到4秒的时间内初始化，平均规模误差为5.3％。该初始化已集成到Orb-Slam视觉持续性中，从而提高了其稳健性和效率，同时保持了其出色的准确性。
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.
