Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring problem with the event camera and generating clear images at high frame-rate. However, the original EDI is mainly designed for offline computation and does not support real-time requirement in many robotics applications. In this paper, we propose the fast EDI, an efficient implementation of EDI that can achieve real-time online computation on single-core CPU devices, which is common for physical robotic platforms used in practice. In experiments, our method can handle event rates at as high as 13 million event per second in a wide variety of challenging lighting conditions. We demonstrate the benefit on multiple downstream real-time applications, including localization, visual tag detection, and feature matching.

翻译：运动去模糊是许多基于视觉的机器人应用中一个关键的病态问题。近期提出的事件相机双积分理论框架为解决去模糊问题并生成高帧率清晰图像提供了理论基础。然而，原始事件双积分主要针对离线计算设计，无法满足众多机器人应用的实时性需求。本文提出快速事件双积分方法，该方法通过高效实现事件双积分算法，可在实际机器人平台常用的单核CPU设备上实现实时在线计算。实验结果表明，在多种具有挑战性的光照条件下，本方法可处理高达每秒1300万事件的事件速率。我们进一步展示了该方法在多个下游实时应用中的优势，包括定位、视觉标签检测和特征匹配。