Abstract—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 dualthreshold ground filtering and principal components analysis. Then the registration between the current frame and the local submap is accomplished efficiently by the proposed multimetric 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 backend, 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 six types of LiDAR on various outdoor and indoor scenarios. On the KITTI benchmark, MULLS ranks among the top LiDARonly SLAM systems with real-time performance.
Summarized novelties or contributions:
This paper proposed a new improved LOAM. I have to say this is almost a new milestone for the LOAM. In this paper, several additional features are extracted from the raw point cloud map. In the conventional LOAM, the features only involve the edge and the planar features and the degenerate case can occur when both the features are limited.
This paper proposed to use the sub-map to sub-map-based matching to refine the pose estimation. This is another novelty that can relax the potential of the point clouds.