Extraction of road boundary from MLS data using laser scanner ground trajectory

被引:6
|
作者
Sui, Lichun [1 ]
Zhu, Jianfeng [1 ,2 ]
Zhong, Mianqing [3 ]
Wang, Xue [4 ]
Kang, Junmei [1 ]
机构
[1] Changan Univ, Coll Geol Engn & Geomat, Xian 710054, Shaanxi, Peoples R China
[2] Jiangxi Coll Appl Technol, Ganzhou 341000, Peoples R China
[3] Lanzhou Jiaotong Univ, Fac Geomat, Lanzhou 730070, Peoples R China
[4] Xianyang Normal Univ, Coll Resources Environm & Hist Culture, Xianyang 712025, Peoples R China
基金
中国国家自然科学基金;
关键词
edge block; scanner ground track; pseudo-mileage spacing map; boundary tracking; CURB DETECTION METHOD; AUTOMATED EXTRACTION; FEATURES; SURFACE; SEGMENTATION; RECOGNITION; MARKINGS;
D O I
10.1515/geo-2020-0264
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Various means of extracting road boundary from mobile laser scanning data based on vehicle trajectories have been investigated. Independent of positioning and navigation data, this study estimated the scanner ground track from the spatial distribution of the point cloud as an indicator of road location. We defined a typical edge block consisting of multiple continuous upward fluctuating points by abrupt changes in elevation, upward slope, and road horizontal slope. Subsequently, such edge blocks were searched for on both sides of the estimated track. A pseudo-mileage spacing map was constructed to reflect the variation in spacing between the track and edge blocks over distance, within which road boundary points were detected using a simple linear tracking model. Experimental results demonstrate that the ground trajectory of the extracted scanner forms a smooth and continuous string just on the road; this can serve as the basis for defining edge block and road boundary tracking algorithms. The defined edge block has been experimentally verified as highly accurate and strongly noise resistant, while the boundary tracking algorithm is simple, fast, and independent of the road boundary model used. The correct detection rate of the road boundary in two experimental data is more than 99.2%.
引用
收藏
页码:690 / 704
页数:15
相关论文
共 50 条
  • [31] An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept
    Cabo, Carlos
    Kukko, Antero
    Garcia-Cortes, Silverio
    Kaartinen, Harri
    Hyyppa, Juha
    Ordonez, Celestino
    REMOTE SENSING, 2016, 8 (09)
  • [32] 3-D Road Boundary Extraction From Mobile Laser Scanning Data via Supervoxels and Graph Cuts
    Zai, Dawei
    Li, Jonathan
    Guo, Yulan
    Cheng, Ming
    Lin, Yangbin
    Luo, Huan
    Wang, Cheng
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 19 (03) : 802 - 813
  • [33] Automatic Road Centerline Extraction from Imagery Using Road GPS Data
    Cao, Chuqing
    Sun, Ying
    REMOTE SENSING, 2014, 6 (09): : 9014 - 9033
  • [34] Wavelet-based extraction of building features from airborne laser scanner data
    Vu, TT
    Tokunaga, M
    Yamazaki, F
    CANADIAN JOURNAL OF REMOTE SENSING, 2003, 29 (06) : 783 - 791
  • [35] Ground extraction from airborne laser data based on wavelet analysis
    Xu, Liang
    Yang, Yan
    Jiang, Bowen
    Li, Jia
    MIPPR 2007: MULTISPECTRAL IMAGE PROCESSING, 2007, 6787
  • [36] Pedestrian trajectory classification method by machine learning using data of laser-scanner tracking system
    Kaneko H.
    Osaragi T.
    Journal of Environmental Engineering (Japan), 2017, 82 (742): : 1051 - 1059
  • [37] Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data
    Yu, Yongtao
    Li, Jonathan
    Guan, Haiyan
    Wang, Cheng
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2015, 16 (04) : 2167 - 2181
  • [38] Ground Segmentation From Large-Scale Terrestrial Laser Scanner Data of Industrial Environments
    Giorgini, M.
    Barbieri, F.
    Aleotti, J.
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2017, 2 (04): : 1948 - 1955
  • [39] Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory
    Elseicy, Ahmed
    Nikoohemat, Shayan
    Peter, Michael
    Elberink, Sander Oude
    REMOTE SENSING, 2018, 10 (11):
  • [40] Ground Penetrating Radar (GPR) and Mobile Laser Scanner (MLS) Technologies for non-destructive analysis of transport infrastructures
    Ciampoli, Luca Bianchini
    Calvi, Alessandro
    Di Benedetto, Alessandro
    Fiani, Margherita
    Gagliardi, Valerio
    EARTH RESOURCES AND ENVIRONMENTAL REMOTE SENSING/GIS APPLICATIONS XII, 2021, 11863