# Aerial Imagery based LIDAR Localization for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2003.11192 · HTML: https://mlsanity.com/paper/2003.11192/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer](https://mlsanity.com/paper/2307.08015/) (2023)
- [Satellite Image Based Cross-view Localization for Autonomous Vehicle](https://mlsanity.com/paper/2207.13506/) (2022)
- [View Consistent Purification for Accurate Cross-View Localization](https://mlsanity.com/paper/2308.08110/) (2023)
- [Weakly-supervised Camera Localization by Ground-to-satellite Image Registration](https://mlsanity.com/paper/2409.06471/) (2024)
- [Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors](https://mlsanity.com/paper/2203.03334/) (2022)
- [Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems](https://mlsanity.com/paper/2206.13883/) (2022)
- [Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings](https://mlsanity.com/paper/2207.14042/) (2022)
- [View-Invariant Localization using Semantic Objects in Changing Environments](https://mlsanity.com/paper/2209.14426/) (2022)
- [Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data](https://mlsanity.com/paper/2503.13896/) (2025)
- [Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization](https://mlsanity.com/paper/2306.17529/) (2023)

## Abstract (arXiv)

This paper presents a localization technique using aerial imagery maps and LIDAR based ground reflectivity for autonomous vehicles in urban environments. Traditional localization techniques using LIDAR reflectivity rely on high definition reflectivity maps generated from a mapping vehicle. The cost and effort required to maintain such prior maps are generally very high because it requires a fleet of expensive mapping vehicles. In this work we propose a localization technique where the vehicle localizes using aerial/satellite imagery, eradicating the need to develop and maintain complex high-definition maps. The proposed technique has been tested on a real world dataset collected from a test track in Ann Arbor, Michigan. This research concludes that aerial imagery based maps provides real-time localization performance similar to state-of-the-art LIDAR based maps for autonomous vehicles in urban environments at reduced costs.