Neuromorphic vision sensors or event cameras have made the visual perception
of extremely low reaction time possible, opening new avenues for high-dynamic
robotics applications. These event cameras' output is dependent on both motion
and texture. However, the event camera fails to capture object edges that are
parallel to the camera motion. This is a problem intrinsic to the sensor and
therefore challenging to solve algorithmically. Human vision deals with
perceptual fading using the active mechanism of small involuntary eye
movements, the most prominent ones called microsaccades. By moving the eyes
constantly and slightly during fixation, microsaccades can substantially
maintain texture stability and persistence. Inspired by microsaccades, we
designed an event-based perception system capable of simultaneously maintaining
low reaction time and stable texture. In this design, a rotating wedge prism
was mounted in front of the aperture of an event camera to redirect light and
trigger events. The geometrical optics of the rotating wedge prism allows for
algorithmic compensation of the additional rotational motion, resulting in a
stable texture appearance and high informational output independent of external
motion. The hardware device and software solution are integrated into a system,
which we call Artificial MIcrosaccade-enhanced EVent camera (AMI-EV). Benchmark
comparisons validate the superior data quality of AMI-EV recordings in
scenarios where both standard cameras and event cameras fail to deliver.
Various real-world experiments demonstrate the potential of the system to
facilitate robotics perception both for low-level and high-level vision tasks.

通过在事件相机的光圈前安装旋转楔形棱镜，我们设计了一种基于仿生微眼球震动的事件感知系统，能够同时维持低反应时间和稳定的纹理外观。AMI-EV 硬件设备和软件解决方案被集成到一个系统中，并在标准相机和事件相机均无法提供满意结果的场景中得到了验证。多种真实世界实验证明该系统在低级别和高级别视觉任务中促进机器人感知的潜力。