Abstract
Accurate relative positioning is crucial for swarm aerial robotics, enabling coordinated flight and collision avoidance. Although vision-based tracking has been extensively studied, 3D LiDAR-based methods remain underutilized despite their robustness under varying lighting conditions. Existing systems often rely on bulky, power-intensive sensors, making them impractical for small UAVs with strict payload and energy constraints. This paper presents a lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework. Our approach effectively addresses the challenges posed by sparse, noisy, and nonuniform point cloud data generated by non-repetitive scanning 3D LiDARs, ensuring reliable tracking while remaining suitable for small drones with strict payload constraints. Unlike conventional filtering techniques, the proposed method dynamically adjusts the noise covariance matrices using innovation and residual statistics, thereby enhancing tracking accuracy under real-world conditions. Additionally, a recovery mechanism ensures continuity of tracking during temporary detection failures caused by scattered LiDAR returns or occlusions. Experimental validation was performed using a Livox Mid-360 LiDAR mounted on a DJI F550 UAV in real-world flight scenarios. The proposed method demonstrated robust UAV tracking performance under sparse LiDAR returns and intermittent detections, consistently outperforming both standard Kalman filtering and particle filtering approaches during aggressive maneuvers. These results confirm that the framework enables reliable relative positioning in GPS-denied environments without the need for multi-sensor arrays or external infrastructure.
Abstract (translated)
精确的相对定位对于集群无人机至关重要，它能够实现协调飞行和碰撞避免。尽管基于视觉的追踪技术已经得到了广泛研究，但由于其在不同光照条件下表现出的强大鲁棒性，3D 激光雷达（LiDAR）方法仍被很大程度上忽视。现有的系统常常依赖于体积庞大且耗电量高的传感器，这使得它们对于受到负载和能量限制的小型无人机来说并不实用。本文提出了一种轻量级的基于 LiDAR 的无人机追踪系统，该系统采用了自适应扩展卡尔曼滤波（AEKF）框架。我们的方法有效地解决了由非重复扫描 3D 激光雷达产生的稀疏、噪声大且分布不均点云数据带来的挑战，确保了可靠的追踪，并适用于严格负载限制的小型无人机。 与传统的滤波技术不同，所提出的方法能够根据创新和残差统计动态调整噪声协方差矩阵，从而在实际条件下提高追踪精度。此外，恢复机制保证了在激光雷达回波分散或遮挡导致的临时检测失败期间跟踪的连续性。 实验验证是在真实的飞行场景中通过将Livox Mid-360 激光雷达安装在 DJI F550 无人机上来完成的。所提出的方法显示出了出色的无人机追踪性能，在稀疏激光雷达返回和间歇性检测下依然稳健，且在剧烈机动期间持续优于标准卡尔曼滤波器和粒子滤波方法。 这些结果证实了该框架能够在无需多传感器阵列或外部基础设施的情况下为 GPS 信号缺失的环境提供可靠的相对定位。
URL
https://arxiv.org/abs/2603.09783
