  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.
