论文标题
蜂群：分散的蜂群惯性进程
Swarm-LIO: Decentralized Swarm LiDAR-inertial Odometry
论文作者
论文摘要
准确的自我和相对状态估计是完成群体任务的关键前提，例如协作自主探索，目标跟踪，搜索和救援。本文提出了空中群体群体群体群体估计方法的群：每架无人机都会执行精确的自我状态估计，通过无线通信交换自我状态和相互观察信息，并根据（W.R.T.）实时和基于LidareTial Insertial Insertial Insertial intertial niterertial niterertial niterertial niterertial seriure估计相对状态。提出了一种基于3D激光雷达的新型无人机检测，识别和跟踪方法，以获得队友无人机的观察。然后，将相互观察测量与IMU和LIDAR测量紧密耦合，以实时和准确地估计自我状态和相对状态。广泛的现实世界实验表明，对复杂场景的广泛适应性，包括被GPS贬低的场景，相机的退化场景（漆黑的夜晚）或激光雷达（面对单个墙）。与运动捕获系统提供的地面真相相比，结果显示了厘米级的定位精度，该准确性优于单个UAV系统的其他最先进的激光惯性探针。
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.
