GroundGrid:LiDAR Point Cloud Ground Segmentation and Terrain Estimation

TL;DR

GroundGrid is a LiDAR point cloud ground segmentation system that uses 2D elevation maps, outperforming state-of-the-art methods with high accuracy and real-time performance for autonomous vehicle perception.

Contribution

We introduce GroundGrid, a novel system leveraging 2D elevation maps for efficient and accurate ground segmentation and terrain estimation in LiDAR point clouds.

Findings

Achieves 94.78% IoU in ground segmentation

Operates at 171Hz for real-time applications

Outperforms existing state-of-the-art methods

Abstract

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