Traditional visual-inertial state estimation targets absolute camera poses
and spatial landmark locations while first-order kinematics are typically
resolved as an implicitly estimated sub-state. However, this poses a risk in
velocity-based control scenarios, as the quality of the estimation of
kinematics depends on the stability of absolute camera and landmark coordinates
estimation. To address this issue, we propose a novel solution to tight
visual-inertial fusion directly at the level of first-order kinematics by
employing a dynamic vision sensor instead of a normal camera. More
specifically, we leverage trifocal tensor geometry to establish an incidence
relation that directly depends on events and camera velocity, and demonstrate
how velocity estimates in highly dynamic situations can be obtained over short
time intervals. Noise and outliers are dealt with using a nested two-layer
RANSAC scheme. Additionally, smooth velocity signals are obtained from a tight
fusion with pre-integrated inertial signals using a sliding window optimizer.
Experiments on both simulated and real data demonstrate that the proposed tight
event-inertial fusion leads to continuous and reliable velocity estimation in
highly dynamic scenarios independently of absolute coordinates. Furthermore, in
extreme cases, it achieves more stable and more accurate estimation of
kinematics than traditional, point-position-based visual-inertial odometry.

我们提出了一种在一级运动学水平上进行紧密视觉惯性融合的新颖解决方案，通过使用动态视觉传感器而不是普通相机，通过利用三焦几何来建立与事件和相机速度直接相关的关联关系，并展示了如何在高度动态的情况下在短时间间隔内获得速度估计。噪声和异常值使用嵌套的两层 RANSAC 方法处理，通过与预积分惯性信号进行紧密融合并使用滑动窗口优化器获得平滑速度信号。对模拟和真实数据的实验证明，所提出的紧密事件惯性融合可以在高度动态的情景中连续且可靠地估计速度，与绝对坐标无关。此外，在极端情况下，它比传统的基于点位置的视觉惯性测距法实现了更稳定和更准确的运动学估计。