Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected autonomous vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.

翻译：激光雷达技术的进步使其生产成本更加经济，同时精度和分辨率得到提升。因此，激光雷达已通过正态分布变换（NDT）及其他先进的三维配准算法，实现厘米级精度，成为车辆定位的核心组成部分。然而，这些方法依赖高精度三维点云地图，而此类地图的构建需投入大量成本。当地图不可用或缺乏足够特征以支持三维配准算法时，定位精度会下降，从而对道路安全构成威胁。为解决这一问题，我们提出利用配备激光雷达的路侧单元与车路通信（Vehicle-to-Infrastructure, V2I）技术，精确估计网联自动驾驶车辆的位置，并在车辆自定位精度不足时提供辅助。仿真结果表明，该方法在定位精度上优于传统的基于NDT扫描匹配的方法。