Accurate self and relative state estimation are the critical preconditions for completing swarm tasks, e.g., collaborative autonomous exploration, target tracking, search and rescue. This paper proposes Swarm-LIO: a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements. A novel 3D LiDAR-based drone detection, identification and tracking method is proposed to obtain observations of teammate drones. The mutual observation measurements are then tightly-coupled with IMU and LiDAR measurements to perform real-time and accurate estimation of ego-state and relative state jointly. Extensive real-world experiments show the broad adaptability to complicated scenarios, including GPS-denied scenes, degenerate scenes for camera (dark night) or LiDAR (facing a single wall). Compared with ground-truth provided by motion capture system, the result shows the centimeter-level localization accuracy which outperforms other state-of-the-art LiDAR-inertial odometry for single UAV system.

翻译：精确的自状态和相对状态估计是完成集群任务（如协同自主探索、目标跟踪、搜索与救援）的关键前提。本文提出Swarm-LIO：一种完全去中心化的空中集群系统状态估计方法，其中每架无人机基于激光雷达-惯性测量，实时执行精确的自状态估计，通过无线通信交换自状态与相互观测信息，并估计相对于其余无人机的相对状态。提出一种新颖的基于三维激光雷达的无人机检测、识别与跟踪方法，以获取队友无人机的观测数据。随后将相互观测测量值与IMU和激光雷达测量值紧耦合，实现对自状态和相对状态的联合实时精确估计。大量真实世界实验表明，该方法对复杂场景（包括GPS拒止场景、相机退化场景（暗夜）或激光雷达退化场景（正对单面墙壁））具有广泛适应性。与运动捕捉系统提供的真值相比，结果显示其定位精度达到厘米级，优于面向单无人机系统的其他先进激光雷达-惯性里程计方法。