四足机器人在图书馆实现高精度自主导航，应对狭窄通道与动态障碍。
Autonomous Navigation System for Library Service Robot Based on Unitree Go2 Edu
- 融合4D激光雷达与深度相机，基于RTAB-Map实现视觉-LiDAR定位
- 在静止、低密度和高密度动态场景中成功率分别达100%、96%、88%
- 适用于对通行空间敏感的室内场景，如图书馆、医院等
图书馆需部署能在狭窄过道中安静移动且不危及读者、座椅、行李与推车的自主机器人。本文针对配备4D激光雷达、前向深度相机和IMU的Unitree Go2 Edu四足机器人，设计了一套基于ROS 2的导航系统。不同于将图书馆视为粗糙地形的假设，本工作聚焦实际部署中的移动断点，包括地面过渡、临时杂物堆积及部分阻塞通道，这些对低离地间隙轮式平台尤为挑战。系统采用RTAB-Map进行视觉-LiDAR SLAM，AMCL与基于EKF的传感器融合实现定位，结合Nav2框架中的A*与DWA算法完成路径规划与局部避障。在真实图书馆环境中，系统在静态、低密度动态与高密度动态场景下的任务成功率达100%、96%和88%；地图验证与实测控制距离对比显示，平均度量误差为3.7厘米。
原文摘要 · Abstract (English)
Libraries require autonomous robots to move quietly through narrow aisles while remaining safe around readers, chairs, bags, and carts. This paper presents a ROS 2 navigation system for a Unitree Go2 Edu quadruped equipped with a 4D LiDAR, a front depth camera, and an IMU. Rather than assuming the library is rough terrain, we target the practical mobility discontinuities of real deployments, including floor transitions, temporary clutter, and partially blocked passages where low-clearance wheeled platforms are less tolerant. RTAB-Map is used for visual-LiDAR SLAM, AMCL and EKF-based sensor fusion provide localization, and a Nav2 stack with A* and DWA supports planning and local avoidance. In a real library, the system achieves 100%, 96%, and 88% success rates in static, low-density dynamic, and high-density dynamic scenes, while map validation against surveyed control distances yields a mean metric error of 3.7 cm.
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