Automated driving systems use multi-modal sensor suites to ensure the reliable, redundant and robust perception of the operating domain, for example camera and LiDAR. An accurate extrinsic calibration is required to fuse the camera and LiDAR data into a common spatial reference frame required by high-level perception functions. Over the life of the vehicle the value of the extrinsic calibration can change due physical disturbances, introducing an error into the high-level perception functions. Therefore there is a need for continuous online extrinsic calibration algorithms which can automatically update the value of the camera-LiDAR calibration during the life of the vehicle using only sensor data. We propose using mutual information between the camera image's depth estimate, provided by commonly available monocular depth estimation networks, and the LiDAR pointcloud's geometric distance as a optimization metric for extrinsic calibration. Our method requires no calibration target, no ground truth training data and no expensive offline optimization. We demonstrate our algorithm's accuracy, precision, speed and self-diagnosis capability on the KITTI-360 data set.

翻译：自动驾驶系统采用多模态传感器套件（例如相机与激光雷达）以确保对运行环境的可靠、冗余且鲁棒的感知。实现相机与激光雷达数据融合至高级感知功能所需的公共空间参考框架，需要精确的外参标定。车辆使用过程中，外参标定值可能因物理扰动发生变化，从而给高级感知功能引入误差。因此，亟需一种仅利用传感器数据即可在车辆生命周期内自动更新相机-激光雷达外参标定值的连续在线外参标定算法。我们提出利用相机图像深度估计（由常见的单目深度估计网络提供）与激光雷达点云几何距离之间的互信息作为外参标定的优化指标。该方法无需标定靶标、无需真实标注训练数据，也无需昂贵的离线优化。我们在KITTI-360数据集上验证了该算法的精度、准度、速度及自诊断能力。