# Autonomous Vision-based UAV Landing with Collision Avoidance using Deep Learning

arXiv: https://arxiv.org/abs/2109.08628 · HTML: https://mlsanity.com/paper/2109.08628/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors](https://mlsanity.com/paper/2007.09785/) (2020)

## Built on by (most cited first, 3 of 3)

- [HIPPo: Harnessing Image-to-3D Priors for Model-free Zero-shot 6D Pose Estimation](https://mlsanity.com/paper/2502.10606/) (2025)
- [L-PR: Exploiting LiDAR Fiducial Marker for Unordered Low Overlap Multiview Point Cloud Registration](https://mlsanity.com/paper/2406.03298/) (2024)
- [SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy](https://mlsanity.com/paper/2605.12247/) (2026)

## Abstract (arXiv)

There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process.