Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds
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Abstract (arXiv)
Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans for accurate UAV tracking in real-world scenarios. Our approach segments point clouds into foreground and background, analyzes spatial-temporal data, and employs a scoring mechanism to enhance detection accuracy. Tested on a public dataset, our solution placed 4th in the CVPR 2024 UG2+ Challenge, demonstrating its practical effectiveness. We plan to open-source all designs, code, and sample data for the research community github.com/lianghanfang/UnLiDAR-UAV-Est.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 19 of its 60 references that are in the map. First posted on arXiv 17 Dec 2024.
Builds on
19 of 60 references.
References · 19
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7 more
- 2019Detection and Tracking of Small Objects in Sparse 3D Laser Range Datacited by 4
- 2020VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approachcited by 4
- 2021Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Cloudscited by 4
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- 2020SPINS: Structure Priors aided Inertial Navigation Systemcited by 2
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.