# Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research

arXiv: https://arxiv.org/abs/2409.15914 · HTML: https://mlsanity.com/paper/2409.15914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Experimental Evaluation of Visual-Inertial Odometry Systems for Arable Farming](https://mlsanity.com/paper/2206.05066/) (2022)
- [Active SLAM: A Review On Last Decade](https://mlsanity.com/paper/2212.11654/) (2022)
- [On-the-Fly SfM: What you capture is What you get](https://mlsanity.com/paper/2309.11883/) (2023)

## Abstract (arXiv)

UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habitat mapping, surveillance and monitoring solutions. High-quality real-time scene reconstruction as well as real-time UAV localization can optimize the exploration vs exploitation balance of single or collaborative mission. In this work, we explore the potential of two collaborative frameworks - Visual Simultaneous Localization and Mapping (V-SLAM) and Structure-from-Motion (SfM) for 3D mapping purposes and compare results with standard offline approaches.