This paper presents a range-aided LiDAR-inertial multi-vehicle mapping system (RaLI-Multi). Firstly, we design a multi-metric weights LiDAR-inertial odometry by fusing observations from an inertial measurement unit (IMU) and a light detection and ranging sensor (LiDAR). The degenerate level and direction are evaluated by analyzing the distribution of normal vectors of feature point clouds and are used to activate the degeneration correction module in which range measurements correct the pose estimation from the degeneration direction. We then design a multi-vehicle mapping system in which a centralized vehicle receives local maps of each vehicle and range measurements between vehicles to optimize a global pose graph. The global map is broadcast to other vehicles for localization and mapping updates, and the centralized vehicle is dynamically fungible. Finally, we provide three experiments to verify the effectiveness of the proposed RaLI-Multi. The results show its superiority in degeneration environments

翻译：本文提出了一种基于测距的激光雷达-惯性多车建图系统（RaLI-Multi）。首先，我们通过融合惯性测量单元（IMU）和激光雷达（LiDAR）的观测数据，设计了一种多度量权重的激光雷达-惯性里程计。通过分析特征点云法向量分布来评估退化程度和方向，并据此激活退化校正模块，利用测距观测对退化方向上的位姿估计进行修正。随后，我们设计了一种多车建图系统，其中中心化车辆接收每辆车的局部地图以及车辆间的测距数据，以优化全局位姿图。全局地图被广播至其他车辆用于定位与建图更新，且中心化车辆可动态切换。最后，我们通过三项实验验证了所提RaLI-Multi的有效性。结果表明该系统在退化环境下具有优越性能。