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：在线相机-地面标定是指实时生成相机与路面之间的非刚体变换。现有解决方案采用静态标定，易受环境变化影响，例如轮胎气压变化、车辆载荷变化以及路面多样性。其他在线方案利用道路元素或图像间重叠视图的光度一致性，这需要持续检测道路上的特定目标或多个摄像头的辅助以完成标定。在我们的工作中，我们提出了一种在线单目相机-地面标定方案，该方案在行驶过程中无需使用任何特定目标。我们采用从粗到精的方法，通过轮式里程计进行地面特征提取，并基于滑动窗口因子图优化来估计相机-地面标定参数。针对行驶过程中相机-地面的非刚体变换，我们提供了量化标定性能的指标以及用于报告/广播满意标定结果的停止准则。使用真实数据进行的广泛实验表明，我们的算法是有效的，并且优于现有最先进技术。