UE-Extractor: A Grid-to-Point Ground Extraction Framework for Unstructured Environments Using Adaptive Grid Projection
Ruoyao Li, Yafei Wang, Shi Sun, Yichen Zhang, Fei Ding, Hongbo Gao
Abstract
Ground point cloud extraction is crucial for route planning of autonomous vehicles in unstructured environments. However, mainstream point cloud extraction methods are susceptible to inaccuracies due to the indistinct obstacle-ground boundary. Furthermore, addressing uneven feature distribution usually necessitates region segmentation, which increases computational demands. To achieve a balance between efficiency and accuracy in ground extraction, we propose a two-stage framework based on adaptive bin partition and grid projection. Firstly, the point cloud is divided into bins based on point cloud distribution and then projected onto the uniform grids, ensuring robust consideration of uneven features. Subsequently, the grid-based coarse extraction is performed by analyzing the grid characteristics to enable a rapid preliminary extraction. Furthermore, the coarse results are reprojected into point cloud form, and the ground-obstacle boundary regions are finally refined by incorporating elevation and curvature probabilities. Evaluations on RELLIS-3D dataset and field tests conducted across typical unstructured scenarios demonstrate that the proposed method achieves promising extraction performance compared to state-of-the-art methods.