Abstract
Mobile laser scanning systems confirmed the capability for detailed roadway documentation. Hand in hand with enormous datasets acquired by these systems is the increase in the demands on the fast and effective processing of these datasets. The crucial part of the roadway datasets processing, as well as in many other applications, is the extraction of objects of interest from point clouds. In this work, an approach to the rough classification of mobile laser scanning data based on raster image processing techniques is presented. The developed method offers a solution for a computationally low demanding classification of the highway environment. The aim of this method is to provide a background for the easier use of more sophisticated algorithms and a specific analysis. The method is evaluated using different metrics on a 1.8km long dataset obtained by LYNX Mobile Mapper over a highway.
Highlights
Mobile laser scanning (MLS), a highly efficient tool for acquiring dense point clouds, has been accepted as a standard for the mapping of road corridors
The solution can be found in the rough classification of the point cloud into several basic classes, which serve as a background for a further sophisticated analysis
The concept of the method for a rough point cloud classification of roadway scenes is based on the following premises: (1.) The point cloud classification problem can be transformed into an image classification problem, which enables the application of techniques for the image processing of the classification and reduce the computational demands of spatial queries
Summary
Mobile laser scanning (MLS), a highly efficient tool for acquiring dense point clouds, has been accepted as a standard for the mapping of road corridors. It is applicable in areas, such as project planning, project development, construction, operations, maintenance, safety, research, and asset management [1]. The high accuracy of these narrowly focused sophisticated methods is often related to high computational demands. These demands can be decreased by decreasing the number of input points. The solution can be found in the rough classification of the point cloud into several basic classes, which serve as a background for a further sophisticated analysis
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