  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.
