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：准确的自我和相对状态估算是完成群落任务的关键先决条件,例如合作自主勘探、目标跟踪、搜索和救援。本文件提议Swarm-LIO:对空中群落系统采用完全分散的国家估算方法:对空中群落系统采用完全分散的国家估算方法,其中,每架无人驾驶飞机进行精确的自我状态估算,通过无线通信交换自我状态和相互观测信息,对无人驾驶航空器的其余部分进行(w.r.t.)实时且仅以LiDAR天体测量为依据的相对状态估算。提出了基于3DLIDAR的无人驾驶飞机探测、识别和跟踪新颖方法,以获得对团队型无人驾驶飞机的观测。然后将相互观测测量方法与IMU和LIDAR的测量方法紧密结合,以进行实时准确的自我状态和相对状态估算。广泛的实体实验显示对复杂情景的广泛适应性,包括GPS密度的场景、摄像的退化场(dark之夜)或LIDAR(形成单面墙 ) 。与移动式采集系统提供的地面巡视系统提供的地面巡测,结果显示其他水平的亚阵度。</s>