Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative SLAM estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing SLAM tasks. In order to contribute to the research community, we will make our code open-source and accessible at \url{https://github.com/PengYu-team/Co-LRIO}.

翻译：利用不同异构传感器进行协同状态估计是GPS受限环境下机器人集群运行的基本前提，也是极具挑战性的研究课题。本文提出一种集中式系统，用于实现协同激光雷达-测距-惯性状态估计，使机器人集群无需布置锚点即可运行。该系统将计算密集型任务高效分配至中央服务器，从而减轻各机器人本地里程计计算的负担。服务器后端通过共享数据构建全局参考框架，并借助闭环检测、全局优化技术及异常数据剔除来优化联合位姿图优化，确保协同状态估计的精确性与鲁棒性。基于公开数据集与自建数据集的全面评估表明，本系统显著提升了协同SLAM估计的精度。此外，该系统在大规模任务中展现出卓越性能，可无缝支持十台机器人高效协作完成SLAM任务。为促进学术社区发展，我们将在\url{https://github.com/PengYu-team/Co-LRIO}开源相关代码。