Paper Info
Title
Extracting road surface from mobile laser scanning point clouds in large scale urban environment
Abstract
This paper presents a novel automatic road surface extraction method from mobile laser scanning point clouds of large scale complex urban environment. First, the candidate road surface points are automatically separated from the whole point cloud and divided into independent local patches by utilizing the trajectory. Then, each local patch is modeled as a 3D plane and the model parameters are estimated through the generalized projection based M-estimator (GPBM). The points of each local patch are classified into two classes, where points lying within the model are classified as road surface and the others are non-road surface. Finally, a fast clustering method is performed on the road surface class to filter out false positives. The remaining points of all patches constitute the road surface points. Experimental results demonstrate the effectiveness and reliability of the proposed algorithm for automatic road surface extraction in complex urban environments.
Year
DOI
Venue
2014
10.1109/ITSC.2014.6958157
ITSC
Keywords
Field
DocType
feature extraction,reliability,roads,gpbm,generalized projection based m-estimator,large scale urban environment,mobile laser scanning point clouds,road surface extraction,cluster analysis,trajectory,algorithms,laser radar
Computer vision,Mobile laser scanning,Simulation,Urban environment,Lidar,Road surface,Artificial intelligence,Engineering,Point cloud,Cluster analysis,Trajectory,False positive paradox
Conference
Citations 
PageRank 
References 
1
0.37
17
Authors
5
Name
Order
Citations
PageRank
Hanyun Wang11166.74
Cheng Wang221832.63
Yiping Chen314820.86
Wentao Yang410.71
Jonathan Li5798119.18