  The combination of LiDARs and cameras enables a mobile robot to perceive
environments with multi-modal data, becoming a key factor in achieving robust
perception. Traditional frame cameras are sensitive to changing illumination
conditions, motivating us to introduce novel event cameras to make LiDAR-camera
fusion more complete and robust. However, to jointly exploit these sensors, the
challenging extrinsic calibration problem should be addressed. This paper
proposes an automatic checkerboard-based approach to calibrate extrinsics
between a LiDAR and a frame/event camera, where four contributions are
presented. Firstly, we present an automatic feature extraction and checkerboard
tracking method from LiDAR's point clouds. Secondly, we reconstruct realistic
frame images from event streams, applying traditional corner detectors to event
cameras. Thirdly, we propose an initialization-refinement procedure to estimate
extrinsics using point-to-plane and point-to-line constraints in a
coarse-to-fine manner. Fourthly, we introduce a unified and globally optimal
solution to address two optimization problems in calibration. Our approach has
been validated with extensive experiments on 19 simulated and real-world
datasets and outperforms the state-of-the-art.
