针对城市环境激光雷达点云地面分割过程中坡度路面、障碍物和地面交界处存在欠分割与过分割的问题,提出一种应用于不同城市场景的地面分割算法。该算法首先利用激光雷达水平角分辨率将点云进行线序化排列,再利用射线前后点的距离比去除悬空异常噪点;随后借助射线点距离与坡度信息自适应调整高度阈值;最后利用调整后的全局与局部高度阈值进行地面分割。对3种不同类型的城市路面进行的实验验证了本文算法的有效性,该算法在不同城市场景下均可区分障碍物与地面交界处的点云与坡面,平均分割准确度达98%,平均耗时2 ms。

We propose a ground segmentation algorithm for various urban environments to overcome the problems of under-segmentation and over-segmentation on sloped roads, obstacles and ground junctions. First, the point cloud is linearly arranged based on the horizontal angular resolution of the lidar. Then, the ratio of the distance from front and rear points to the lidar is used to remove abnormal noise, and the height threshold is adaptively adjusted by using the distance from each point to the lidar and slope value. Finally, ground segmentation is performed using the adjusted global and local height thresholds. Through experimental analysis of three different types of urban roads, it is verified that the proposed algorithm can effectively distinguish the point cloud between the obstacle and ground and slope surfaces in different urban scenarios. The segmentation accuracy can reach 98% on average, and the average time consumed is 2 ms.