针对从三维激光雷达点云中准确实时地分割地面的问题，提出一种基于扫描线段特征的地面分割算法。算法首先对三维点云进行去噪和位姿修正，接着依据相邻点间的欧氏距离和绝对高度差分割扫描线，然后对扫描线段的相邻线段间距、倾斜度、绝对高度差等特征进行分析，采用最大似然估计法求解特征阈值函数，提高了阈值的自适应性；最后综合考虑起伏、倾斜等复杂地形，通过制定横、纵向分类策略将扫描线标记为平坦地面线段、坡面线段和障碍物线段。本算法已成功应用在地面无人平台上，使用情况和对比试验表明，在城市和野外场景中，本算法都能够稳定高效地分割地面。

Aiming at the problem of accurately segmenting the ground in real-time from 3D LiDAR point cloud, a ground segmentation algorithm based on the features of scanning line segments is proposed. The algorithm first performs de-noising and pose correction on the 3D point cloud, then divides the scanning line according to the Euclidean distance and absolute height difference between adjacent points. Next, the characteristics of the adjacent line segments such as spacing, slope, and absolute height difference are analyzed. The maximum likelihood estimation is used to solve the feature threshold function, which improves the adaptability of threshold. Finally, comprehensively considering the undulating and inclined complex terrain, the scanning line segments are marked as segments of flat ground, segments of slope and segments of obstacle by formulating the new horizontal and vertical classification strategies. This algorithm has been successfully applied to the unmanned ground platform. The usage and comparative test show that the algorithm can detect the ground stably and efficiently in both urban and field scenarios.