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.

翻译：LiDAR与相机的结合使移动机器人能够利用多模态数据感知环境，已成为实现鲁棒感知的关键因素。传统帧相机对光照条件变化敏感，这促使我们引入新型事件相机，使LiDAR-相机融合更加完整和鲁棒。然而，为联合利用这些传感器，需解决具有挑战性的外参标定问题。本文提出一种基于棋盘格的自动方法，用于标定LiDAR与帧/事件相机之间的外参，主要贡献包括四点：首先，提出一种从LiDAR点云中自动提取特征并追踪棋盘格的方法；其次，利用事件流重建逼真的帧图像，将传统角点检测器应用于事件相机；第三，提出一种初始化-优化流程，通过点面约束和点线约束以从粗到精的方式估计外参；第四，引入统一且全局最优的解，以处理标定中的两个优化问题。该方法在19个模拟与真实数据集上进行了充分实验验证，性能优于现有最先进方法。