The precise point cloud ground segmentation is a crucial prerequisite of virtually all perception tasks for LiDAR sensors in autonomous vehicles. Especially the clustering and extraction of objects from a point cloud usually relies on an accurate removal of ground points. The correct estimation of the surrounding terrain is important for aspects of the drivability of a surface, path planning, and obstacle prediction. In this article, we propose our system GroundGrid which relies on 2D elevation maps to solve the terrain estimation and point cloud ground segmentation problems. We evaluate the ground segmentation and terrain estimation performance of GroundGrid and compare it to other state-of-the-art methods using the SemanticKITTI dataset and a novel evaluation method relying on airborne LiDAR scanning. The results show that GroundGrid is capable of outperforming other state-of-the-art systems with an average IoU of 94.78% while maintaining a high run-time performance of 171Hz. The source code is available at https://github.com/dcmlr/groundgrid

翻译：精确的点云地面分割是自动驾驶车辆中激光雷达传感器几乎所有感知任务的关键前提。特别是点云中物体的聚类与提取通常依赖于准确的地面点移除。周围地形的正确估计对于路面可行驶性评估、路径规划和障碍物预测等方面至关重要。本文提出基于二维高程地图解决地形估计与点云地面分割问题的GroundGrid系统。我们使用SemanticKITTI数据集和基于机载激光雷达扫描的新型评估方法，对GroundGrid的地面分割与地形估计性能进行评估，并与现有先进方法进行对比。结果表明，GroundGrid能够以94.78%的平均交并比优于其他先进系统，同时保持171Hz的高运行效率。源代码发布于https://github.com/dcmlr/groundgrid。