Event camera, a bio-inspired asynchronous triggered camera, offers promising prospects for fusion with frame-based cameras owing to its low latency and high dynamic range. However, calibrating stereo vision systems that incorporate both event and frame-based cameras remains a significant challenge. In this letter, we present EF-Calib, a spatiotemporal calibration framework for event- and frame-based cameras using continuous-time trajectories. A novel calibration pattern applicable to both camera types and the corresponding event recognition algorithm is proposed. Leveraging the asynchronous nature of events, a derivable piece-wise B-spline to represent camera pose continuously is introduced, enabling calibration for intrinsic parameters, extrinsic parameters, and time offset, with analytical Jacobians provided. Various experiments are carried out to evaluate the calibration performance of EF-Calib, including calibration experiments for intrinsic parameters, extrinsic parameters, and time offset. Experimental results show that EF-Calib achieves the most accurate intrinsic parameters compared to current SOTA, the close accuracy of the extrinsic parameters compared to the frame-based results, and accurate time offset estimation. EF-Calib provides a convenient and accurate toolbox for calibrating the system that fuses events and frames. The code of this paper will also be open-sourced at: https://github.com/wsakobe/EF-Calib.

翻译：事件相机作为一种受生物启发的异步触发相机，凭借其低延迟和高动态范围的特性，为与基于帧的相机融合提供了广阔前景。然而，标定同时包含事件相机和帧相机的立体视觉系统仍是一个重大挑战。本文提出EF-Calib，一种利用连续时间轨迹对事件相机与帧相机进行时空标定的框架。我们提出了一种适用于两种相机类型的新型标定图案及相应的事件识别算法。利用事件的异步特性，引入了可微分的分段B样条来连续表示相机位姿，从而能够对标定内参、外参和时间偏移，并提供了解析雅可比矩阵。我们进行了多种实验以评估EF-Calib的标定性能，包括内参、外参和时间偏移的标定实验。实验结果表明，与当前最先进方法相比，EF-Calib实现了最精确的内参标定，其外参标定精度接近基于帧的标定结果，并能准确估计时间偏移。EF-Calib为融合事件与帧的系统提供了一个便捷且精确的标定工具箱。本文代码亦将开源发布于：https://github.com/wsakobe/EF-Calib。