Autonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Inspired by the need for real-life deployment of autonomous robots in such environments, this article presents an experimental comparative study of 3D SLAM algorithms. The study focuses on state-of-the-art Lidar SLAM algorithms with open-source implementation that are i) lidar-only like BLAM, LOAM, A-LOAM, ISC-LOAM and hdl graph slam, or ii) lidar-inertial like LeGO-LOAM, Cartographer, LIO-mapping and LIO-SAM. The evaluation of the methods is performed based on a dataset collected from the Boston Dynamics Spot robot equipped with 3D lidar Velodyne Puck Lite and IMU Vectornav VN-100, during a mission in an underground tunnel. In the evaluation process poses and 3D tunnel reconstructions from SLAM algorithms are compared against each other to find methods with most solid performance in terms of pose accuracy and map quality.

翻译：在缺乏自然光照或光照不足、GPS信号受限的恶劣地下（SubT）环境中实现机器人自主导航是一项具有挑战性的任务，这促进了位姿估计与地图构建算法的发展。受此类环境中自主机器人实际部署需求的驱动，本文对3D SLAM算法进行了实验对比研究。研究聚焦于具有开源实现的最先进激光雷达SLAM算法，包括：i）纯激光雷达类算法，如BLAM、LOAM、A-LOAM、ISC-LOAM和hdl graph slam；ii）激光雷达-惯性融合类算法，如LeGO-LOAM、Cartographer、LIO-mapping和LIO-SAM。评估基于波士顿动力Spot机器人在地下隧道任务中采集的数据集进行，该机器人搭载了3D激光雷达Velodyne Puck Lite和惯性测量单元Vectornav VN-100。在评估过程中，通过对比各SLAM算法输出的位姿与3D隧道重建结果，确定了在位姿精度与地图质量方面表现最为稳健的方法。