This paper presents a framework for the targetless extrinsic calibration of stereo cameras and Light Detection and Ranging (LiDAR) sensors with a non-overlapping Field of View (FOV). In order to solve the extrinsic calibrations problem under such challenging configuration, the proposed solution exploits road markings as static and robust features among the various dynamic objects that are present in urban environment. First, this study utilizes road markings that are commonly captured by the two sensor modalities to select informative images for estimating the extrinsic parameters. In order to accomplish stable optimization, multiple cost functions are defined, including Normalized Information Distance (NID), edge alignment and, plane fitting cost. Therefore a smooth cost curve is formed for global optimization to prevent convergence to the local optimal point. We further evaluate each cost function by examining parameter sensitivity near the optimal point. Another key characteristic of extrinsic calibration, repeatability, is analyzed by conducting the proposed method multiple times with varying randomly perturbed initial points.

翻译：本文为无目标的音响照相机和光探测和测距(LiDAR)传感器提供了一个框架,该传感器具有非重叠的视野(FOV),目的是解决在这种富有挑战性的配置下外部校准问题,拟议的解决方案将道路标志作为城市环境中各种动态物体的静态和稳健特征。首先,本研究报告利用两种传感器模式通常捕捉的道路标记选择信息图像,以估计外源参数。为了实现稳定优化,确定了多种成本功能,包括标准化信息距离(NID)、边缘校正和平面装配成本。因此,为全球优化形成了一条通畅的成本曲线,以防止与当地最佳点的趋同。我们进一步通过在最佳点附近检查参数敏感性来评估每一项成本功能。通过对外源校准(重复性)的另一个关键特征进行分析,方法是多次采用不同随机扰动初始点的拟议方法。