This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing...This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing a voxel model;extracting the road surface points by employing the voxel-based segmentation algorithm;refining the road boundary using the curb-based segmentation algorithm.To evaluate the accuracy of the proposed method,the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used.The proposed algorithm extracted the road surface successfully with high accuracy.There was an average recall of 99.5%,the precision was 96.3%,and the F1 score was 97.9%.From the extracted road surface,a framework for the estimation of road roughness was proposed.Good agreement was achieved when comparing the results of the road roughness map with the visual image,indicating the feasibility and effectiveness of the proposed framework.展开更多
基金Project(SIIT-AUN/SEED-Net-G-S1 Y16/018)supported by the Doctoral Asean University Network ProgramProject supported by the Metropolitan Expressway Co.,Ltd.,Japan+2 种基金Project supported by Elysium Co.Ltd.Project supported by Aero Asahi Corporation,Co.,Ltd.Project supported by the Expressway Authority of Thailand。
文摘This paper presents a voxel-based region growing method for automatic road surface extraction from mobile laser scanning point clouds in an expressway environment.The proposed method has three major steps:constructing a voxel model;extracting the road surface points by employing the voxel-based segmentation algorithm;refining the road boundary using the curb-based segmentation algorithm.To evaluate the accuracy of the proposed method,the two-point cloud datasets of two typical test sites in an expressway environment consisting of flat and bumpy surfaces with a high slope were used.The proposed algorithm extracted the road surface successfully with high accuracy.There was an average recall of 99.5%,the precision was 96.3%,and the F1 score was 97.9%.From the extracted road surface,a framework for the estimation of road roughness was proposed.Good agreement was achieved when comparing the results of the road roughness map with the visual image,indicating the feasibility and effectiveness of the proposed framework.
