Open AccessUnstructured road parameter cognition for ICVs using multi‐frame 3D point clouds
Author(s) -
Xie Guotao,
Yan Kangjian,
Wang Dongsheng,
Sun Ning,
Gao Hongbo
Publication year - 2021
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs2.12017
Subject(s) - segmentation , computer science , frame (networking) , point cloud , point (geometry) , computer vision , artificial intelligence , mathematics , geometry , telecommunications
Road parameter cognition is essential for intelligent and connected vehicles (ICVs) operating on unstructured roads because it can influence their path planning and motion control. A method is presented for the extraction of longitudinal slopes and lateral slopes and the roughness of unstructured roads using multi‐frame three‐dimensional point clouds. First, a segmentation method based on the multiple features is proposed to extract and divide the regions of interest into blocks with different slopes, which is valuable for further study on ground segmentation. Second, a parameter cognition method is proposed to estimate the slope and roughness of unstructured roads. Last but not least, a multi‐frame fusion method is proposed to improve cognition accuracy. Experimental tests on unstructured roads demonstrate that the proposed algorithm has satisfactory performance in terms of the accuracy of recognizing road slopes and roughness.