Online camera-to-ground calibration is to generate a non-rigid body transformation between the camera and the road surface in a real-time manner. Existing solutions utilize static calibration, suffering from environmental variations such as tire pressure changes, vehicle loading volume variations, and road surface diversity. Other online solutions exploit the usage of road elements or photometric consistency between overlapping views across images, which require continuous detection of specific targets on the road or assistance with multiple cameras to facilitate calibration. In our work, we propose an online monocular camera-to-ground calibration solution that does not utilize any specific targets while driving. We perform a coarse-to-fine approach for ground feature extraction through wheel odometry and estimate the camera-to-ground calibration parameters through a sliding-window-based factor graph optimization. Considering the non-rigid transformation of camera-to-ground while driving, we provide metrics to quantify calibration performance and stopping criteria to report/broadcast our satisfying calibration results. Extensive experiments using real-world data demonstrate that our algorithm is effective and outperforms state-of-the-art techniques.

翻译：在线摄像头到地面的标定需要实时生成相机与道路表面之间的非刚性变换。现有的解决方案利用静态标定，受到轮胎气压变化、车辆装载量变化和道路表面多样性等环境变化的影响。其他在线解决方案利用了道路元素或图像之间的光度一致性，需要在道路上持续检测特定目标或利用多个摄像头来促进标定。在我们的工作中，我们提出了一种在线单目摄像头到地面的标定解决方案，不需要在行驶过程中使用任何特定的目标。我们通过轮胎测速计执行粗到细的地面特征提取方法，并通过基于滑动窗口的因子图优化来估计摄像头到地面的标定参数。考虑到在行驶过程中摄像头到地面的非刚性变换，我们提供了指标来量化标定性能，并提供停止标准来报告/广播我们令人满意的标定结果。使用真实世界的数据进行广泛的实验，证明我们的算法是有效的，并且优于现有的技术。