スパース点群を用いた教師なしUAV 3次元軌道推定
Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds
複数のLiDARスキャンを時空間的に融合し、教師なしで小型UAVの3次元軌道を高精度に推定する手法を提案。CVPR 2024 UG2+チャレンジで4位に入賞した。
著者: Hanfang Liang, Yizhuo Yang, Jinming Hu, Jianfei Yang, Fen Liu, Shenghai Yuan
分類: cs.CV, cs.RO
原文アブストラクト
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.
