Abstract
Urban areas 3D model reconstruction is one of the major fields of application of 3D scanning technologies. In the future, vehicle-based laser scanning, here called mobile laser scanning system, should see considerable use for 3D road environment modelling in urban areas. In this context, one of the main limitations perceived by the mobile laser scanning system is the incompleteness of the sampling. Whenever we scan urban area road environment, the produced sampling usually presents a large number of missing regions. Many algorithmic solutions exist to close those gaps from specific hole filling algorithms to the drastic solution of using water-tight reconstruction methods. In this paper, a method for filling holes of road surface point clouds and generating 3D model of road surface from mobile laser scanning data is developed. The data is classified into road surface, on-road and off-road surface point clouds. Many large holes in the road surface point clouds are filled by using data assimilation algorithm. Then, the road surface is 3D modeled as a triangulated irregular network. It is shown that the whole road surface 3D model is integrated after data processing. The above mentioned steps are applied to a large set of mobile laser scanning data of urban area road environment, in order to obtain the whole urban road surface 3D model.
Highlights
As scientists, engineers and planners require ever more detailed information about our urban areas the requirement for accurate 3D mapping of road and city structures is likely to increase considerably in the years to come
The objective of this paper is to develop a method for automatically road surface point clouds detecting and road surface 3D modelling, and present a data assimilation algorithm for filling holes in road surface point clouds from mobile laser scanning data
We can make sure that those point clouds are off-surface points which should be assigned as point clouds of no interest; (2) After step (1), we find out the top point of road surface point clouds, and the height (z) value of the top point is applied as a height threshold
Summary
Engineers and planners require ever more detailed information about our urban areas the requirement for accurate 3D mapping of road and city structures is likely to increase considerably in the years to come. In order to enhance the implementation of urban areas solutions during the regeneration and transformation of cities, advanced digital applications can have a significant impact. A MLS system integrate GPS/INS system and data acquisition sensors on a rigid moving platform for collecting laser scanning point clouds from the surroundings of the mapping system have been developed since the early 1990s. MLS data-sets are including the application of vegetation analysis [12,13,14], but best for detecting objects of urban areas, e.g., walls of building and collecting even more information from road surface [15], In the case of urban areas the detection and quantification of road surface is important for the implementation of urban areas solutions during the regeneration and transformation of cities. On the other hand urban road surface models are needed for accurate three-dimensional mapping of urban areas
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