Event cameras triggered a paradigm shift in the computer vision community delineated by their asynchronous nature, low latency, and high dynamic range. Calibration of event cameras is always essential to account for the sensor intrinsic parameters and for 3D perception. However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor. The current standard for calibrating event cameras relies on either blinking patterns or event-based image reconstruction algorithms. These approaches are difficult to deploy in factory settings and are affected by noise and artifacts degrading the calibration performance. To bridge these limitations, we present E-Calib, a novel, fast, robust, and accurate calibration toolbox for event cameras utilizing the asymmetric circle grid, for its robustness to out-of-focus scenes. E-Calib introduces an efficient reweighted least squares (eRWLS) method for feature extraction of the calibration pattern circles with sub-pixel accuracy and robustness to noise. In addition, a modified hierarchical clustering algorithm is devised to detect the calibration grid apart from the background clutter. The proposed method is tested in a variety of rigorous experiments for different event camera models, on circle grids with different geometric properties, on varying calibration trajectories and speeds, and under challenging illumination conditions. The results show that our approach outperforms the state-of-the-art in detection success rate, reprojection error, and pose estimation accuracy.

中文翻译：

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E-Calib：用于事件摄像机的快速、稳健且准确的校准工具箱


事件相机引发了计算机视觉社区的范式转变，其异步特性、低延迟和高动态范围引起了人们的关注。事件摄像机的校准对于考虑传感器内在参数和 3D 感知始终至关重要。然而，由于传感器的异步二进制输出，传统的基于图像的校准技术不适用。当前校准事件相机的标准依赖于闪烁模式或基于事件的图像重建算法。这些方法很难在工厂设置中部署，并且会受到噪声和伪影的影响，从而降低校准性能。为了弥补这些限制，我们推出了 E-Calib，这是一种新颖、快速、稳健且准确的校准工具箱，适用于利用不对称圆形网格的事件摄像机，因其对失焦场景的鲁棒性。 E-Calib 引入了一种高效的重新加权最小二乘 (eRWLS) 方法，用于校准图案圆的特征提取，具有亚像素精度和抗噪声鲁棒性。此外，设计了一种改进的层次聚类算法来检测校准网格，使其远离背景杂波。所提出的方法在针对不同事件相机模型、具有不同几何属性的圆形网格、不同的校准轨迹和速度以及具有挑战性的照明条件下的各种严格实验中进行了测试。结果表明，我们的方法在检测成功率、重投影误差和姿态估计精度方面优于最先进的方法。