  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.
