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}]$. In this project, we followed the LeGO-LOAM, a light-weighted real-time SLAM system that extracts and registers ground as an addition to the original LOAM, and we proposed a new clustering-based method to refine the planar extraction algorithm for ground such that the system can handle much more noisy or dynamic environments. We implemented this method and compared it with LeGo-LOAM on our collected data of CMU campus, as well as a collected dataset for ATV (All-Terrain Vehicle) for off-road self-driving. Both visualization and evaluation results show obvious improvement of our algorithm.
翻译：近些年来,仅使用点云的SLAM系统被证明是成功的。在大多数这些系统中,它们提取了地面清除后跟踪跟踪功能,这在 z- 轴上造成了很大的差异。 地面实际上提供了可靠的信息以获取 [t_z,\theta ⁇ roll},\theta ⁇ ç ⁇ pitch}]$。 在这个项目中,我们跟踪了LEGO-LOAM,这是一个轻量级实时SLAM系统,它提取和登记地面,作为原始LOAM的补充,我们提出了一个新的基于集群的方法来改进地面的平面提取算法,使系统能够处理更多的噪音或动态环境。我们应用了这种方法,并在我们收集的CMU校园数据上将其与LeGo-LOAM进行了比较,并为ATV(全Train车)收集了一套数据,用于远程自我驱动。视觉和评价结果都显示了我们算法的明显改进。
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