Fusing LiDAR and high definition (HD) maps is a feasible way to achieve global localization in GNSS-denied environments, which is necessary in driverless vehicle and robotic technologies. This paper proposes a single-shot global localization solution that uses only a single current scan of a rotating multiple-beam LiDAR sensor to locate its own location and pose with respect to an HD map in the form of georeferenced point clouds. This single-shot global localization solution estimates the state of the current moment without the previous moment state and thus avoids the nonconvergence problems that plague filter-based methods. The proposed solution allows HD maps from diverse LiDAR sensors to be used for global localization and is more robust than existing methods. The proposed solution consists of two procedures: offline preprocessing and online global localization. In the offline procedure, diverse HD maps are preprocessed to construct a global prior map for the localization process. The online global localization procedure includes two elements: place recognition, location and pose estimation. A novel Cross-Section Shape Context (CSSC) descriptor that is highly descriptive and rotation-invariant is proposed for subsequent processes. Two strategies, two-stage similarity estimation and Nearest Cluster Distance Ratio (NCDR), based on the CSSC descriptor are proposed to improve place recognition precision. A Selective Generalized Iterative Closest Point (SGICP) algorithm is proposed to calculate location and pose accurately using the CSSC descriptor. Comprehensive experiments were performed to evaluate this solution. A comparison of the precision-recall curve of multiple scenes, particularly under changed viewpoint scenes, shows that the CSSC descriptor is more robust than existing descriptors. Experimental analysis also confirms that the proposed strategies, two-stage similarity estimation and NCDR, improve place recognition precision. Also, compared to the generalized iterative closest point algorithm, the SGICP algorithm achieved better accuracy by 31% and efficiency by 60%. The proposed solution achieves a mean relative translation error (RTE) improvement of 27% over the OneShot algorithm on the KITTI dataset. The proposed solution had an average 77% improvement over 1σ RTE relative to the benchmark in tests with the long-term localization NCLT dataset. The mean RTE of the proposed solution was 0.13 m using HD maps from different LiDAR sensors. Our code is available at: https://github.com/Dongxu05/CSSC.

融合 LiDAR 和高清 (HD) 地图是在 GNSS 拒绝环境中实现全球定位的可行方法，这在无人驾驶车辆和机器人技术中是必要的。本文提出了一种单次全局定位解决方案，该解决方案仅使用旋转多光束 LiDAR 传感器的单次当前扫描来定位其自身的位置，并以地理参考点云的形式相对于高清地图的姿势。这种单次全局定位解决方案在没有前一时刻状态的情况下估计当前时刻的状态，从而避免了困扰基于滤波器的方法的不收敛问题。所提出的解决方案允许将来自不同 LiDAR 传感器的高清地图用于全球定位，并且比现有方法更稳健。建议的解决方案包括两个程序：离线预处理和在线全球本地化。在离线过程中，对不同的高清地图进行预处理，以构建用于定位过程的全局先验地图。在线全球定位过程包括两个元素：位置识别、位置和姿势估计。为后续过程提出了一种具有高度描述性和旋转不变性的新型横截面形状上下文 (CSSC) 描述符。提出了基于CSSC描述符的两阶段相似性估计和最近聚类距离比（NCDR）两种策略来提高地点识别精度。提出了一种选择性广义迭代最近点（SGICP）算法，利用CSSC描述符准确计算位置和姿态。进行了综合实验来评估该解决方案。多个场景的精确召回曲线的比较，特别是在变化的视点场景下，表明 CSSC 描述符比现有描述符更鲁棒。实验分析还证实，所提出的策略，两阶段相似性估计和 NCDR，提高了地点识别精度。此外，与广义迭代最近点算法相比，SGICP算法的准确率提高了31%，效率提高了60%。与 KITTI 数据集上的 OneShot 算法相比，所提出的解决方案实现了 27% 的平均相对平移误差 (RTE) 改进。相对于使用长期定位 NCLT 数据集的测试中的基准，所提出的解决方案比 1σ RTE 平均提高了 77%。使用来自不同 LiDAR 传感器的高清地图，所提出的解决方案的平均 RTE 为 0.13 m。