The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classified feature points (ground, facade, pillar, beam, etc.) are extracted from each frame using dual-threshold ground filtering and principal components analysis. Then the registration between the current frame and the local submap is accomplished efficiently by the proposed multi-metric linear least square iterative closest point algorithm. Point-to-point (plane, line) error metrics within each point class are jointly optimized with a linear approximation to estimate the ego-motion. Static feature points of the registered frame are appended into the local map to keep it updated. For the back-end, hierarchical pose graph optimization is conducted among regularly stored history submaps to reduce the drift resulting from dead reckoning. Extensive experiments are carried out on three datasets with more than 100,000 frames collected by seven types of LiDAR on various outdoor and indoor scenarios. On the KITTI benchmark, MULLS ranks among the top LiDAR-only SLAM systems with real-time performance.

翻译：自动驾驶和移动绘图的快速开发,即适合不同复杂情景下不同规格的LIDAR不同规格的LIDAR的LIDAR SLAM 快速开发自动驱动和移动绘图功能;为此目的,我们提议采用MULLS,一个高效、低驾驶和多功能的3DLIDAR SLAM系统;对于前端,通过双临界地面过滤和主要组成部分分析,从每个框架抽取了大致分类的特征点(地面、外墙、界柱、梁柱、梁梁等),以不断更新这些特征点;然后,通过拟议的多米线最小迭接点最接近点的最接近点计算法,使当前框架和本地子图之间的登记效率得以实现。每个点的点对点(平线、线)误差度测量仪以直线近度法联合优化,以估计自我感动。已登记框架的静态特征点附在当地地图中;对于后端,在定期储存的历史子图中进行等级表优化,以减少由死数计算造成的漂浮流。在三个数据集上进行了广泛的实验,由7个直径直径直径的S-MALLAR系统收集的直径100 000MLVD。