arXiv 2412.127162025-01-19cs.CVcs.RO

基于稀疏点云的无监督无人机三维轨迹估计

Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds

Jianghan University（江汉大学）；Nanyang Technological University（南洋理工大学）

Hanfang Liang, Yizhuo Yang, Jinming Hu, Jianfei Yang, Fen Liu, Shenghai Yuan

备注：: This paper has been accepted for presentation at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2025. 2025 IEEE Trademark. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

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AI总结：

针对小型无人机难以探测的安全问题，提出一种基于时空序列处理融合多激光雷达扫描的无监督无人机探测方法，在CVPR 2024挑战赛中获得第四名，并将开源代码。

摘要：

紧凑型无人机系统在推进投递和监控应用的同时，由于其体积小，难以被传统方法探测，带来了显著的安全挑战。本文提出了一种经济高效的无监督无人机探测方法，利用时空序列处理融合多个激光雷达扫描，以实现真实场景中无人机的精确跟踪。我们的方法将点云分割为前景和背景，分析时空数据，并采用评分机制来提高探测精度。在公共数据集上的测试中，我们的解决方案在CVPR 2024 UG2+挑战赛中排名第四，证明了其实用有效性。我们计划向研究社区开源所有设计、代码和示例数据，网址为github.com/lianghanfang/UnLiDAR-UAV-Est。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。