Sensor fusion is vital for the safe and robust operation of autonomous vehicles. Accurate extrinsic sensor to sensor calibration is necessary to accurately fuse multiple sensor's data in a common spatial reference frame. In this paper, we propose a target free extrinsic calibration algorithm that requires no ground truth training data, artificially constrained motion trajectories, hand engineered features or offline optimization and that is accurate, precise and extremely robust to initialization error. Most current research on online camera-LiDAR extrinsic calibration requires ground truth training data which is impossible to capture at scale. We revisit analytical mutual information based methods first proposed in 2012 and demonstrate that geometric features provide a robust information metric for camera-LiDAR extrinsic calibration. We demonstrate our proposed improvement using the KITTI and KITTI-360 fisheye data set.

翻译：传感器融合对于自动驾驶车辆的安全与稳健运行至关重要。精确的传感器间外参标定是实现多传感器数据在统一空间参考系中准确融合的必要前提。本文提出一种无目标外参标定算法，无需真实标注训练数据、人工约束运动轨迹、手工设计特征或离线优化，且具有高精度、高准确度以及对初始误差的极强鲁棒性。当前大多数在线相机-激光雷达外参标定研究依赖真实标注训练数据，而这类数据难以大规模采集。我们重新审视了2012年首次提出的基于解析互信息的方法，并证明几何特征可为相机-激光雷达外参标定提供稳健的信息度量。基于KITTI与KITTI-360鱼眼数据集，我们验证了所提改进方法的有效性。