  SLAM system using only point cloud has been proven successful in recent
years. In most of these systems, they extract features for tracking after
ground removal, which causes large variance on the z-axis. Ground actually
provides robust information to obtain [t_z, \theta_{roll}, \theta_{pitch}]$. In
this project, we followed the LeGO-LOAM, a light-weighted real-time SLAM system
that extracts and registers ground as an addition to the original LOAM, and we
proposed a new clustering-based method to refine the planar extraction
algorithm for ground such that the system can handle much more noisy or dynamic
environments. We implemented this method and compared it with LeGo-LOAM on our
collected data of CMU campus, as well as a collected dataset for ATV
(All-Terrain Vehicle) for off-road self-driving. Both visualization and
evaluation results show obvious improvement of our algorithm.
