An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

被引：25

作者：

Cabo, Carlos ^{[1
]}

Kukko, Antero ^{[2
]}

Garcia-Cortes, Silverio ^{[1
]}

Kaartinen, Harri ^{[2
]}

Hyyppa, Juha ^{[2
]}

Ordonez, Celestino ^{[1
]}

机构：

[1] Univ Oviedo, Dept Min Exploitat, Oviedo 33004, Spain

[2] Finnish Geospatial Res Inst FGI, POB 15, FI-02431 Masala, Finland

来源：

REMOTE SENSING | 2016年 / 8卷 / 09期

基金：

芬兰科学院;

关键词：

mobile laser scanning; 3D road extraction; scanning lines; road edges; smoothing window; road management; line clouds; EXTRACTION; OBJECTS;

D O I：

10.3390/rs8090740

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping from point cloud laser data. Using geometric criteria, the initial 3D LiDAR point data is structured in lines covering the road surface. These lines are then grouped according to a set of quasi-planar restriction rules. Road asphalt edge limits are extracted from the end points of lines belonging to these groups. Finally a two-stage smoothing procedure is applied to correct for edge occlusions and other anomalies. The method was tested on a 2.1 km stretch of road, and the results were checked using a RTK-GNSS measured dataset as ground truth. Correctness and completeness were 99% and 97%, respectively.

引用

页数：20

共 36 条

[1]

[Anonymous], 2009, P INT ARCH PHOT REM

[2]

[Anonymous], P 22 AS C REM SENS S

[3]

[Anonymous], 1973, Cartographica: the international journal for geographic information and geovisualization, DOI [DOI 10.3138/FM57-6770-U75U-7727, 10.3138/FM57-6770-U75U-7727]

[4]

[Anonymous], 2004, P INT SOC PHOTOGRAMM

[5]

Axelsson P., 2000, The International Archives of the Photogrammetry and Remote Sensing, Amsterdam, The Netherlands, VXXXIII, P110, DOI DOI 10.1016/J.ISPRSJPRS.2005.10.005

[6] Unsupervised robust planar segmentation of terrestrial laser scanner point clouds based on fuzzy clustering methods [J].