论文标题
通过迭代的扩展Kalman滤波器估计直接视觉惯性的自我运动估计
Direct Visual-Inertial Ego-Motion Estimation via Iterated Extended Kalman Filter
论文作者
论文摘要
这封信提出了一种反应性导航策略，以恢复微型航空车的高度，翻译速度和方向。主要贡献在于惯性测量单元（IMU）测量的直接和紧密融合，并在单个平面场景的假设下与单眼反馈。采用了迭代的扩展卡尔曼过滤器（IEKF）方案。状态预测使用IMU读数，而状态更新直接依赖于光度反馈作为测量。与基于特征的方法不同，创新项的光度差异在一个步骤中呈现出固有且可靠的数据关联过程。使用现实世界数据集对所提出的方法进行验证。结果表明，所提出的方法比基于特征的方法提供了更好的鲁棒性，准确性和效率。进一步的研究表明，提出方法的飞行速度估计值的准确性与两个最先进的视觉惯性系统（VIN）相媲美，而所提出的框架的准确性则是$ \ \ \ \ 30 $ \ 30 $ tims的速度，这要归因于省略和映射。
This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU) measurements with monocular feedback under an assumption of a single planar scene. An Iterated Extended Kalman Filter (IEKF) scheme is employed. The state prediction makes use of IMU readings while the state update relies directly on photometric feedback as measurements. Unlike feature-based methods, the photometric difference for the innovation term renders an inherent and robust data association process in a single step. The proposed approach is validated using real-world datasets. The results show that the proposed method offers better robustness, accuracy, and efficiency than a feature-based approach. Further investigation suggests that the accuracy of the flight velocity estimates from the proposed approach is comparable to those of two state-of-the-art Visual Inertial Systems (VINS) while the proposed framework is $\approx15-30$ times faster thanks to the omission of reconstruction and mapping.
