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

arXiv: https://arxiv.org/abs/2404.03187 · HTML: https://mlsanity.com/paper/2404.03187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers](https://mlsanity.com/paper/2104.11896/) (2021)
- [OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition](https://mlsanity.com/paper/2203.03397/) (2022)
- [Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator](https://mlsanity.com/paper/2308.16906/) (2023)
- [GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2103.04233/) (2021)
- [SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding](https://mlsanity.com/paper/2306.05407/) (2023)
- [DenseCAvoid: Real-time Navigation in Dense Crowds using Anticipatory Behaviors](https://mlsanity.com/paper/2002.03038/) (2020)
- [GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2209.05722/) (2022)
- [Satellite Image Based Cross-view Localization for Autonomous Vehicle](https://mlsanity.com/paper/2207.13506/) (2022)
- [RSL-Net: Localising in Satellite Images From a Radar on the Ground](https://mlsanity.com/paper/2001.03233/) (2020)
- [Frozone: Freezing-Free, Pedestrian-Friendly Navigation in Human Crowds](https://mlsanity.com/paper/2003.05395/) (2020)
- [City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent](https://mlsanity.com/paper/2203.05612/) (2022)
- [U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization](https://mlsanity.com/paper/2310.13766/) (2023)
- [Any Way You Look At It: Semantic Crossview Localization and Mapping with LiDAR](https://mlsanity.com/paper/2203.08925/) (2022)
- [Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors](https://mlsanity.com/paper/2203.03334/) (2022)
- [CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition](https://mlsanity.com/paper/2303.17778/) (2023)
- [VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain](https://mlsanity.com/paper/2209.07725/) (2022)
- [Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images](https://mlsanity.com/paper/2301.04224/) (2023)
- [VERN: Vegetation-aware Robot Navigation in Dense Unstructured Outdoor Environments](https://mlsanity.com/paper/2303.14502/) (2023)
- [A Concave Optimization Algorithm for Matching Partially Overlapping Point Sets](https://mlsanity.com/paper/1701.00951/) (2017)
- [OF-VO: Efficient Navigation among Pedestrians Using Commodity Sensors](https://mlsanity.com/paper/2004.10976/) (2020)
- [Energy-Based Models for Cross-Modal Localization using Convolutional Transformers](https://mlsanity.com/paper/2306.04021/) (2023)
- [Geographical Map Registration and Fusion of Lidar-Aerial Orthoimagery in GIS](https://mlsanity.com/paper/1904.09047/) (2019)

## Built on by (most cited first, 2 of 2)

- [TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning](https://mlsanity.com/paper/2512.10419/) (2025)
- [GeoISF: Instance Semantic Forest Inspired Large-Scale Cross-View Geo-Localization via Ground LiDAR-to-Satellite Image](https://mlsanity.com/paper/2606.28371/) (2026)

## Abstract (arXiv)

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 data can be accessed at https://github.com/rayguan97/AGL-Net.