Event cameras are bio-inspired sensors that capture per-pixel asynchronous intensity change rather than the synchronous absolute intensity frames captured by a classical camera sensor. Such cameras are ideal for robotics applications since they have high temporal resolution, high dynamic range and low latency. However, due to their high temporal resolution, event cameras are particularly sensitive to flicker such as from fluorescent or LED lights. During every cycle from bright to dark, pixels that image a flickering light source generate many events that provide little or no useful information for a robot, swamping the useful data in the scene. In this paper, we propose a novel linear filter to preprocess event data to remove unwanted flicker events from an event stream. The proposed algorithm achieves over 4.6 times relative improvement in the signal-to-noise ratio when compared to the raw event stream due to the effective removal of flicker from fluorescent lighting. Thus, it is ideally suited to robotics applications that operate in indoor settings or scenes illuminated by flickering light sources.
翻译:事件相机是生物感应传感器,它捕捉到像素无同步强度变化,而不是古典相机传感器所捕捉到的同步绝对强度框架。这些相机对于机器人应用是理想的,因为它们具有高时间分辨率、高动态范围以及低悬浮度。然而,由于其高时间分辨率,事件相机对闪烁作用特别敏感,例如从荧光灯或LED灯。在从亮到暗的每一个周期中,闪烁的光源产生许多事件,为机器人提供很少或没有有用的信息,将有用的数据淹没在现场。在本文中,我们提议对预处理事件数据进行新的线性过滤,以便从事件流中去除不必要的闪烁事件。拟议的算法在与原始事件流相比,由于有效去除荧光灯光,信号到噪音的比率相对提高了4.6倍以上。因此,它非常适合在室内环境或闪烁光源所亮的场景区运行的机器人应用。