AGL-NET: Aerial-Ground Cross-Modal Global Localization with Varying Scales

arXiv:2404.03187v1 Announce Type: new
Abstract: 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 …

abstract aerial arxiv challenges cs.cv feature gap global image lidar localization maps modal novel representation robust satellite scale type view