Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 24 Sep 2024.
Builds on
7 of 29 references.
References · 7
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
- 2023LightGlue: Local Feature Matching at Light Speedcited by 583
- 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
- 2022Experimental Evaluation of Visual-Inertial Odometry Systems for Arable Farmingcited by 5
- 2022Active SLAM: A Review On Last Decadecited by 4
- 2023On-the-Fly SfM: What you capture is What you getcited by 4
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