无人机群用的高效去中心化定位系统，支持自动加入与低带宽通信。
Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms
- 去中心化通信架构，仅交换身份、状态和观测等低维信息。
- 自动检测新成员并初始化时间偏移与全局外参，提升部署效率。
- 结合激光雷达、惯性与相互观测数据，精度高且适应动态群组。
空中无人机群在协同探索、目标跟踪、搜救等方面具有巨大潜力，但高效的自定位与互定位估计仍是关键挑战。本文提出Swarm-LIO2：一种完全去中心化、即插即用、计算高效且带宽友好的激光雷达-惯性里程计系统，适用于空中无人机群。该系统采用去中心化、即插即用的通信网络，仅交换身份、自身状态、相互观测测量和全局外参等低维信息。为支持新成员的无缝加入，Swarm-LIO2可自动检测潜在队友，并完成时间偏移与全局外参的初始化。为此，提出了基于反射率的无人机检测、轨迹匹配及因子图优化方法以提升初始化效率。在状态估计中，采用高效的ESIKF框架融合激光雷达、惯性数据及相互观测信息，并通过精确的时间延迟补偿与测量建模，显著提升定位精度与一致性。
原文摘要 · Abstract (English)
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.
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