As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method between event cameras and lidars. The high dynamic range, high temporal resolution, and low-latency operation of event cameras are exploited to directly register lidar laser returns, allowing information-based correlation methods to optimize for the 6-DoF extrinsic calibration between the two sensors. This paper presents the first direct calibration method between event cameras and lidars, removing dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements.

翻译：随着神经形态技术的逐步成熟，其在机器人和自动驾驶系统中的应用已成为一个活跃的研究领域。特别是在低功耗和低延迟需求应用中，事件相机已发展成为传统帧式相机的有力替代方案。为使事件相机能够与激光雷达等核心传感器协同完成感知任务，我们提出了一种直接且时间解耦的事件相机与激光雷达外参标定方法。通过利用事件相机的高动态范围、高时间分辨率和低延迟特性，直接配准激光雷达的回波信号，使得基于信息量的相关方法能够优化两个传感器之间的6自由度外参标定。本文提出了首个事件相机与激光雷达之间的直接标定方法，消除了对帧式相机中介和/或高精度人工测量的依赖。