To address the urgent need for efficient and accurate state estimation in unmanned aerial vehicle(UAV)swarm systems for tasks such as collaborative exploration

LIO-UAVs

a fully decentralized

plug-and-play

and highly computationally and bandwidth-efficient odometry system that integrates LiDAR and inertial sensors

is proposed.This approach constructs a decentralized communication network that only exchanges low-dimensional identities

states

mutual observation measurements

and global extrinsic parameter transformations.It also incorporates reflectivity-based unmanned aerial vehicle detection

trajectory matching

and factor graph optimization to enable rapid and automatic initialization and time synchronization of new nodes.The system fuses LiDAR

inertial measurement unit(IMU)

and mutual observation data within an error-state iterative Kalman filter(ESIKF)framework

improving estimation accuracy and consistency through precise compensation for time delay and measurement noise.It also leverages global extrinsics parameter to enhance robustness in degraded scenarios.Simulations and field experiments demonstrate that the system maintains centimeter-level positioning accuracy in complex scenarios such as GPS denial and sensor degradation

outperforming existing state-of-the-art methods.