Aerial Imagery based LIDAR Localization for Autonomous Vehicles
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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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Mar 2020.
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Citing papers per year: 2022: 5; 2023: 3; 2024: 1; 2025: 1.
10 papers.
- 2023Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformercited by 45
- 2022Satellite Image Based Cross-view Localization for Autonomous Vehiclecited by 19
- 2023View Consistent Purification for Accurate Cross-View Localizationcited by 15
- 2024Weakly-supervised Camera Localization by Ground-to-satellite Image Registrationcited by 15
- 2022Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensorscited by 10
- 2022Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systemscited by 5
- 2022Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markingscited by 2
- 2022View-Invariant Localization using Semantic Objects in Changing Environmentscited by 2
- 2025Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Datacited by 1
- 2023Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localizationcited by 0
- 2025Evaluating Global Geo-alignment for Precision Learned Autonomous Vehicle Localization using Aerial Data18 Mar 2025
- 2024Weakly-supervised Camera Localization by Ground-to-satellite Image Registration10 Sep 2024
- 2023View Consistent Purification for Accurate Cross-View Localization16 Aug 2023
- 2023Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer16 Jul 2023
- 2023Locking On: Leveraging Dynamic Vehicle-Imposed Motion Constraints to Improve Visual Localization30 Jun 2023
- 2022View-Invariant Localization using Semantic Objects in Changing Environments28 Sep 2022
- 2022Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings28 Jul 2022
- 2022Satellite Image Based Cross-view Localization for Autonomous Vehicle27 Jul 2022
- 2022Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems28 Jun 2022
- 2022Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors7 Mar 2022