# Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data

arXiv: https://arxiv.org/abs/2503.13896 · HTML: https://mlsanity.com/paper/2503.13896/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 45 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching](https://mlsanity.com/paper/2203.14148/) (2022)
- [Ford Multi-AV Seasonal Dataset](https://mlsanity.com/paper/2003.07969/) (2020)
- [Convolutional Cross-View Pose Estimation](https://mlsanity.com/paper/2303.05915/) (2023)
- [Learning to Localize Using a LiDAR Intensity Map](https://mlsanity.com/paper/2012.10902/) (2020)
- [Robust and Precise Vehicle Localization based on Multi-sensor Fusion in Diverse City Scenes](https://mlsanity.com/paper/1711.05805/) (2017)
- [You Are Here: Geolocation by Embedding Maps and Images](https://mlsanity.com/paper/1911.08797/) (2019)
- [RSL-Net: Localising in Satellite Images From a Radar on the Ground](https://mlsanity.com/paper/2001.03233/) (2020)
- [Localization Requirements for Autonomous Vehicles](https://mlsanity.com/paper/1906.01061/) (2019)
- [City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground Agent](https://mlsanity.com/paper/2203.05612/) (2022)
- [Aerial Imagery based LIDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2003.11192/) (2020)
- [Increasing SLAM Pose Accuracy by Ground-to-Satellite Image Registration](https://mlsanity.com/paper/2404.09169/) (2024)
- [BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment](https://mlsanity.com/paper/2405.09001/) (2024)
- [3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation](https://mlsanity.com/paper/1709.00587/) (2017)

## Built on by (most cited first, 1 of 1)

- [TransLocNet: Cross-Modal Attention for Aerial-Ground Vehicle Localization with Contrastive Learning](https://mlsanity.com/paper/2512.10419/) (2025)

## Abstract (arXiv)

Recently there has been growing interest in the use of aerial and satellite map data for autonomous vehicles, primarily due to its potential for significant cost reduction and enhanced scalability. Despite the advantages, aerial data also comes with challenges such as a sensor-modality gap and a viewpoint difference gap. Learned localization methods have shown promise for overcoming these challenges to provide precise metric localization for autonomous vehicles. Most learned localization methods rely on coarsely aligned ground truth, or implicit consistency-based methods to learn the localization task -- however, in this paper we find that improving the alignment between aerial data and autonomous vehicle sensor data at training time is critical to the performance of a learning-based localization system. We compare two data alignment methods using a factor graph framework and, using these methods, we then evaluate the effects of closely aligned ground truth on learned localization accuracy through ablation studies. Finally, we evaluate a learned localization system using the data alignment methods on a comprehensive (1600km) autonomous vehicle dataset and demonstrate localization error below 0.3m and 0.5circ sufficient for autonomous vehicle applications.