基于事件的视觉传感器及其应用综述

基于事件的相机是一种生物启发的新型视觉传感器,可实时高效地捕捉场景的变化.与基于帧的传统相机不同,事件相机仅报告触发的像素级亮度变化(称为事件),并以微秒级分辨率输出异步事件流.该类视觉传感器已经逐渐成为图像处理、计算机视觉、机器人感知与状态估计、神经形态学等领域的研究热点.首先,本文阐述了事件相机的基本原理、发展历程、优势与挑战;然后,介绍了3种典型事件相机(包括DVS(dynamic vision sensor)、ATIS(asynchronous time based image sensor)和DAVIS(dynamic and active pixel vision sensor))以及多种新型事件相机;接下来,重点回顾了事件相机在特征提取、深度估计、光流估计、强度图像估计与三维重建、目标识别与跟踪、自主定位与位姿估计、视觉里程计与SLAM、多传感器融合等方面的应用研究;最后,归纳了事件相机的研究进展,并探讨了未来的发展趋势.

A Review of Event-based Vision Sensors and Their Applications

The event-based camera is a novel bio-inspired vision sensor that efficiently captures scene changes in real-time.Unlike traditional frame-based cameras,event cameras only report triggered pixel-level brightness changes(called events)and outputs asynchronous event streams at microsecond resolution.This type of vision sensor has gradually become a hot topic in the fields of image processing,computer vision,robot perception and state estimation,and neuromorphology.We first describe the basic principles,developmental history,advantages,and challenges of event cameras.Then,three typical event cameras(including the DVS,ATIS and DAVIS)and multiple advanced event cameras are introduced.Next,we review the research on applications of event cameras,including feature extraction,depth estimation,optical flow estimation,intensity image estimation and 3D reconstruction,object recognition and tracking,autonomous localization and pose estimation,visual odometry and SLAM,multi-sensor fusion,and other aspects.Finally,the research progress of the event cameras is summarized and the future development trend is discussed.