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
Digitizing road maps manually is an expensive and time-consuming task. Several methods that intend to develop fully or semi-automated systems have been proposed. In this work we introduce a method, based on the Radon ...Digitizing road maps manually is an expensive and time-consuming task. Several methods that intend to develop fully or semi-automated systems have been proposed. In this work we introduce a method, based on the Radon transform and optimal algorithms, which extracts automatically roads on images of rural areas, images that were acquired by digital cameras and airborne laser scanners. The proposed method detects linear segments iteratively and starting from this it generates the centerlines of the roads. The method is based on an objective function which depends on three parameters related to the correlation between the cross-sections, spectral similarity and directions of the segments. Different tests were performed using aerial photos, Ikonos images and laser scanner data of an area located in the state of Parana （Brazil） and their results are presented and discussed. The quality of the detection of the roads centerlines was computed using several indexes - completeness, correctness and RMS. The values obtained reveal the good performance of the proposed methodology.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.
文摘Digitizing road maps manually is an expensive and time-consuming task. Several methods that intend to develop fully or semi-automated systems have been proposed. In this work we introduce a method, based on the Radon transform and optimal algorithms, which extracts automatically roads on images of rural areas, images that were acquired by digital cameras and airborne laser scanners. The proposed method detects linear segments iteratively and starting from this it generates the centerlines of the roads. The method is based on an objective function which depends on three parameters related to the correlation between the cross-sections, spectral similarity and directions of the segments. Different tests were performed using aerial photos, Ikonos images and laser scanner data of an area located in the state of Parana （Brazil） and their results are presented and discussed. The quality of the detection of the roads centerlines was computed using several indexes - completeness, correctness and RMS. The values obtained reveal the good performance of the proposed methodology.
