# Vision-based GNSS-Free Localization for UAVs in the Wild

arXiv: https://arxiv.org/abs/2210.09727 · HTML: https://mlsanity.com/paper/2210.09727/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization](https://mlsanity.com/paper/2401.05971/) (2024)
- [Visual place recognition for aerial imagery: A survey](https://mlsanity.com/paper/2406.00885/) (2024)
- [Leveraging edge detection and neural networks for better UAV localization](https://mlsanity.com/paper/2404.06207/) (2024)
- [Beyond Matching to Tiles: Bridging Unaligned Aerial and Satellite Views for Vision-Only UAV Navigation](https://mlsanity.com/paper/2603.22153/) (2026)
- [SatFix: Absolute Visual Localization of UAVs in Satellite Maps from a Single Oblique Image](https://mlsanity.com/paper/2610.11049/) (2026)

## Abstract (arXiv)

Considering the accelerated development of Unmanned Aerial Vehicles (UAVs) applications in both industrial and research scenarios, there is an increasing need for localizing these aerial systems in non-urban environments, using GNSS-Free, vision-based methods. Our paper proposes a vision-based localization algorithm that utilizes deep features to compute geographical coordinates of a UAV flying in the wild. The method is based on matching salient features of RGB photographs captured by the drone camera and sections of a pre-built map consisting of georeferenced open-source satellite images. Experimental results prove that vision-based localization has comparable accuracy with traditional GNSS-based methods, which serve as ground truth. Compared to state-of-the-art Visual Odometry (VO) approaches, our solution is designed for long-distance, high-altitude UAV flights. Code and datasets are available at https://github.com/TIERS/wildnav.