Swarm-LIO2：面向无人机集群的去中心化高效激光雷达-惯性里程计
Swarm-LIO2: Decentralized, Efficient LiDAR-inertial Odometry for UAV Swarms
AI总结：
本文提出Swarm-LIO2，一种去中心化、即插即用的无人机集群激光雷达-惯性里程计，通过交换低维信息、自动初始化新队友及ESIKF框架融合多源测量，实现了高效准确的自状态与互状态估计。
摘要：
空中集群系统在协同探索、目标跟踪、搜索与救援等方面具备巨大潜力。高效、准确的自状态与互状态估计是完成这些集群任务的关键前提，目前仍是极具挑战性的研究课题。本文提出Swarm-LIO2：一种面向空中集群系统的完全去中心化、即插即用、计算高效且带宽高效的激光雷达-惯性里程计。Swarm-LIO2采用去中心化、即插即用网络作为通信基础设施。系统仅交换带宽高效且低维度的信息，包括身份标识、自状态、互观测测量以及全局外参变换。为支持新队友参与者的即插即用，Swarm-LIO2会检测潜在队友无人机，并全自动完成时间偏移与全局外参变换的初始化。为提升初始化效率，本文提出了基于反射率的无人机检测、轨迹匹配及因子图优化新方法。在状态估计方面，Swarm-LIO2在高效的ESIKF框架内融合LiDAR、IMU与互观测测量，并对时间延迟进行仔细补偿及对测量进行建模，以增强准确性与一致性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
