We present AGL-NET, a novel learning-based method for global localization using LiDAR point clouds and satellite maps. AGL-NET tackles two critical challenges: bridging the representation gap between image and points modalities for robust feature matching, and handling inherent scale discrepancies between global view and local view. To address these challenges, AGL-NET leverages a unified network architecture with a novel two-stage matching design. The first stage extracts informative neural features directly from raw sensor data and performs initial feature matching. The second stage refines this matching process by extracting informative skeleton features and incorporating a novel scale alignment step to rectify scale variations between LiDAR and map data. Furthermore, a novel scale and skeleton loss function guides the network toward learning scale-invariant feature representations, eliminating the need for pre-processing satellite maps. This significantly improves real-world applicability in scenarios with unknown map scales. To facilitate rigorous performance evaluation, we introduce a meticulously designed dataset within the CARLA simulator specifically tailored for metric localization training and assessment. The code and dataset will be made publicly available.

翻译：我们提出AGL-NET，一种基于学习的创新方法，利用激光雷达点云与卫星地图实现全局定位。该方法攻克两大核心难题：弥合图像与点云模态间的表征差异以实现鲁棒特征匹配，同时处理全局视角与局部视角固有的尺度差异。针对这些挑战，AGL-NET采用统一网络架构并设计新颖的两阶段匹配策略。第一阶段直接从原始传感器数据中提取高信息量的神经特征并执行初始特征匹配；第二阶段通过提取骨架特征并引入创新的尺度对齐步骤来优化匹配过程，以校正激光雷达与地图数据间的尺度变异。此外，新型尺度与骨架损失函数引导网络学习尺度不变的特征表征，无需预处理卫星地图，显著提升了未知地图尺度场景下的实际应用能力。为严格评估性能，我们在CARLA模拟器中精心构建了专为度量定位训练与评估设计的数据集。相关代码与数据集将开源发布。