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The combination of LiDARs and cameras enables a mobile robot to perceiveenvironments with multi-modal data, becoming a key factor in achieving robustperception. Traditional frame cameras are sensitive to changing illuminationconditions, motivating us to introduce novel event cameras to make LiDAR-camerafusion more complete and robust. However, to jointly exploit these sensors, thechallenging extrinsic calibration problem should be addressed. This paperproposes an automatic checkerboard-based approach to calibrate extrinsicsbetween a LiDAR and a frame/event camera, where four contributions arepresented. Firstly, we present an automatic feature extraction and checkerboardtracking method from LiDAR's point clouds. Secondly, we reconstruct realisticframe images from event streams, applying traditional corner detectors to eventcameras. Thirdly, we propose an initialization-refinement procedure to estimateextrinsics using point-to-plane and point-to-line constraints in acoarse-to-fine manner. Fourthly, we introduce a unified and globally optimalsolution to address two optimization problems in calibration. Our approach hasbeen validated with extensive experiments on 19 simulated and real-worlddatasets and outperforms the state-of-the-art.

LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution