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An Algorithm for Automated Estimation of Road Roughness from Mobile Laser Scanning Data
Author(s) -
Kumar Pankaj,
Lewis Paul,
McElhinney Conor P.,
Rahman Alias Abdul
Publication year - 2015
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1111/phor.12090
Subject(s) - point cloud , road surface , lidar , laser scanning , surface roughness , surface (topology) , computer science , grid , surface finish , residual , algorithm , point (geometry) , mobile mapping , elevation (ballistics) , remote sensing , environmental science , computer vision , laser , geodesy , geology , mathematics , materials science , geometry , optics , physics , composite material
Road roughness is the deviation of a road surface from a designed surface grade that influences safety conditions for road users. Mobile laser scanning ( MLS ) systems provide a rapid, continuous and cost‐effective way of collecting highly accurate and dense 3D point‐cloud data along a route corridor. In this paper an algorithm for the automated estimation of road roughness from MLS data is presented, where a surface grid is fitted to the lidar points associated with the road surface. The elevation difference between the lidar points and their surface grid equivalents provides residual values in height which can be used to estimate roughness along the road surface. Tests validated the new road‐roughness algorithm by successfully estimating surface conditions on multiple road sections. These findings contribute to a more comprehensive approach to surveying road networks.