评估多扫描集成在基于无人机激光雷达的跟踪中的性能
Evaluating the Performance of Multi-Scan Integration for UAV LiDAR-based Tracking
AI总结：
本文评估了多扫描集成方法在GNSS受限环境中使用固态激光雷达和卡尔曼滤波器跟踪无人机的性能，结果表明该方法能实现较低的位置误差，是一种可行的跟踪方案。
摘要：
无人机已成为包括农业、测量和交通运输在内的广泛行业中的必备工具。然而，在具有挑战性的环境中（如杂乱或GNSS受限的环境）跟踪无人机仍然是一个关键问题。此外，无人机正作为多机器人系统的一部分部署，其中跟踪其位置对于相对状态估计至关重要。在本文中，我们评估了一种多扫描集成方法在GNSS受限环境中使用固态激光雷达和卡尔曼滤波器（KF）跟踪无人机的性能。我们评估了该算法在大型开放区域中在不同距离和速度下跟踪无人机的能力。我们的定量分析表明，虽然使用恒速模型的“通过检测进行跟踪”是唯一能够持续跟踪目标的方法，但使用卡尔曼滤波器集成多个扫描频率可以实现更低的位置误差，并代表了在类似场景中跟踪无人机的一种可行选择。
英文摘要：
Drones have become essential tools in a wide range of industries, including agriculture, surveying, and transportation. However, tracking unmanned aerial vehicles (UAVs) in challenging environments, such cluttered or GNSS-denied environments, remains a critical issue. Additionally, UAVs are being deployed as part of multi-robot systems, where tracking their position can be essential for relative state estimation. In this paper, we evaluate the performance of a multi-scan integration method for tracking UAVs in GNSS-denied environments using a solid-state LiDAR and a Kalman Filter (KF). We evaluate the algorithm's ability to track a UAV in a large open area at various distances and speeds. Our quantitative analysis shows that while "tracking by detection" using a constant velocity model is the only method that consistently tracks the target, integrating multiple scan frequencies using a KF achieves lower position errors and represents a viable option for tracking UAVs in similar scenarios.
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。
