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 this https URL

本文介绍了一种基于 2D 高程图的地形估计和点云地面分割系统 GroundGrid，并利用 SemanticKITTI 数据集和航空 LiDAR 扫描进行了性能评估，结果显示 GroundGrid 在维持高运行速度的同时，优于其他现有系统，平均 IoU 为 94.78%。