Calibrating the extrinsic parameters of sensory devices is crucial for fusing multi-modal data. Recently, event cameras have emerged as a promising type of neuromorphic sensors, with many potential applications in fields such as mobile robotics and autonomous driving. When combined with LiDAR, they can provide more comprehensive information about the surrounding environment. Nonetheless, due to the distinctive representation of event cameras compared to traditional frame-based cameras, calibrating them with LiDAR presents a significant challenge. In this paper, we propose a novel method to calibrate the extrinsic parameters between a dyad of an event camera and a LiDAR without the need for a calibration board or other equipment. Our approach takes advantage of the fact that when an event camera is in motion, changes in reflectivity and geometric edges in the environment trigger numerous events, which can also be captured by LiDAR. Our proposed method leverages the edges extracted from events and point clouds and correlates them to estimate extrinsic parameters. Experimental results demonstrate that our proposed method is highly robust and effective in various scenes.

翻译：传感器设备外参标定是多模态数据融合的关键环节。近年来，事件相机作为一种新型神经形态传感器崭露头角，在移动机器人、自动驾驶等领域具有广阔应用前景。当与激光雷达联用时，两者可提供更全面的环境感知信息。然而，由于事件相机与传统帧式相机的数据表征存在本质差异，其与激光雷达的联合标定面临重大挑战。本文提出一种无需标定板等辅助设备的新型方法，用于标定事件相机与激光雷达双模态系统之间的外参。该方法基于以下物理规律：当事件相机运动时，环境中反射率变化和几何边缘会触发大量事件，这些边缘信息同样可被激光雷达捕获。所提方法通过提取事件流与点云中的边缘特征实现特征关联，进而估计外参参数。实验结果表明，本方法在多种场景下均展现出高度鲁棒性与有效性。