MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square

@article{Pan2021MULLSVL,
  title={MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square},
  author={Yue Pan and Pengchuan Xiao and Yujie He and Zhenlei Shao and Zesong Li},
  journal={2021 IEEE International Conference on Robotics and Automation (ICRA)},
  year={2021},
  pages={11633-11640}
}
  • Yue PanPengchuan Xiao Zesong Li
  • Published 7 February 2021
  • Environmental Science
  • 2021 IEEE International Conference on Robotics and Automation (ICRA)
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… 
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