Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms

TL;DR

Swarm-LIO2 introduces a decentralized, efficient LiDAR-inertial odometry system for UAV swarms that enables automatic teammate detection, robust state estimation, and bandwidth-efficient communication, facilitating scalable swarm operations.

Contribution

The paper presents a novel decentralized LiDAR-inertial odometry framework with automatic teammate detection and efficient data exchange for UAV swarms, improving scalability and robustness.

Findings

Achieved accurate self and mutual state estimation in UAV swarms.

Demonstrated bandwidth-efficient communication with minimal data exchange.

Validated system performance through extensive experiments.

Abstract

Aerial swarm systems possess immense potential in various aspects, such as cooperative exploration, target tracking, search and rescue. Efficient, accurate self and mutual state estimation are the critical preconditions for completing these swarm tasks, which remain challenging research topics. This paper proposes Swarm-LIO2: a fully decentralized, plug-and-play, computationally efficient, and bandwidth-efficient LiDAR-inertial odometry for aerial swarm systems. Swarm-LIO2 uses a decentralized, plug-and-play network as the communication infrastructure. Only bandwidth-efficient and low-dimensional information is exchanged, including identity, ego-state, mutual observation measurements, and global extrinsic transformations. To support the plug-and-play of new teammate participants, Swarm-LIO2 detects potential teammate UAVs and initializes the temporal offset and global extrinsic transformation all automatically. To enhance the initialization efficiency, novel reflectivity-based UAV detection, trajectory matching, and factor graph optimization methods are proposed. For state estimation, Swarm-LIO2 fuses LiDAR, IMU, and mutual observation measurements within an efficient ESIKF framework, with careful compensation of temporal delay and modeling of measurements to enhance the accuracy and consistency.