# Localization in Aerial Imagery with Grid Maps using LocGAN

arXiv: https://arxiv.org/abs/1906.01540 · HTML: https://mlsanity.com/paper/1906.01540/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

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

- [Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings](https://mlsanity.com/paper/2207.14042/) (2022)
- [Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone](https://mlsanity.com/paper/2512.02737/) (2025)

## Abstract (arXiv)

In this work, we present LocGAN, our localization approach based on a geo-referenced aerial imagery and LiDAR grid maps. Currently, most self-localization approaches relate the current sensor observations to a map generated from previously acquired data. Unfortunately, this data is not always available and the generated maps are usually sensor setup specific. Global Navigation Satellite Systems (GNSS) can overcome this problem. However, they are not always reliable especially in urban areas due to multi-path and shadowing effects. Since aerial imagery is usually available, we can use it as prior information. To match aerial images with grid maps, we use conditional Generative Adversarial Networks (cGANs) which transform aerial images to the grid map domain. The transformation between the predicted and measured grid map is estimated using a localization network (LocNet). Given the geo-referenced aerial image transformation the vehicle pose can be estimated. Evaluations performed on the data recorded in region Karlsruhe, Germany show that our LocGAN approach provides reliable global localization results.