Spatial convolution is arguably the most fundamental of 2D image processing operations. Conventional spatial image convolution can only be applied to a conventional image, that is, an array of pixel values (or similar image representation) that are associated with a single instant in time. Event cameras have serial, asynchronous output with no natural notion of an image frame, and each event arrives with a different timestamp. In this paper, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera. The approach operates on the event stream output of the camera directly without synthesising pseudo-image frames as is common in the literature. The key idea is the introduction of an internal state that directly encodes the convolved image information, which is updated asynchronously as each event arrives from the camera. The state can be read-off as-often-as and whenever required for use in higher level vision algorithms for real-time robotic systems. We demonstrate the application of our method to corner detection, providing an implementation of a Harris corner-response `state' that can be used in real-time for feature detection and tracking on robotic systems.

使用内部状态直接处理事件流，避免合成伪图像帧，实现了事件相机输出与线性空间卷积的计算方法，应用于提取角点等高级视觉算法中并可用于机器人系统的实时特征检测和跟踪。

事件相机的异步空间图像卷积