  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.
