Road Features Extraction Using
Terrestrial Mobile Laser Scanning
PhD thesis, National University of Ireland Maynooth.
In this thesis, we present the experimental research and key contributions we
have made in the field of road feature extraction from LiDAR data. We detail
the development of three automated algorithms for the extraction of road
features from terrestrial mobile LiDAR data. LiDAR data is a rich source
of 3D geo-referenced information whose volume and scale have inhibited the
development of automated algorithms. Automated feature extraction algorithms
enable the wider geospatial industry to transition from traditional
road feature surveying approaches to terrestrial mobile laser scanning technologies.
Our first contribution to this field is an automated road edge extraction
algorithm which can be applied to LiDAR data and navigation information
acquired by mobile survey vehicles. This novel algorithm relies on the combination
of thresholding and a parametric active contour model to precisely
extract road edges. We describe an automated validation algorithm we developed
to determine the accuracy of our road edge extraction algorithm.
Using the extracted road edges, we are able to accurately extract the
road surface from the LiDAR data. This enables us to develop an efficient
automated road marking extraction algorithm which is our second contribution.
Through the thresholding of the intensity values of road surface LiDAR
points, we can extract the road marking LiDAR points. The third contribution
of this thesis is the development of an automated road roughness
estimation algorithm which is also dependent on the accurate detection of
road surface LiDAR points. We fit a surface grid to the LiDAR points representing
an ideal road surface and measure the elevation difference between
this surface and the actual LiDAR points to compute the surface deviation
along a track representing a vehicle wheel.
We automated these algorithms through exhaustive examination of optimal
parameters and methods for their implementation. To verify these novel
algorithms, we tested them on varying types of road sections representing
rural, urban and national primary road sections. The research work carried
out in the course of this thesis provides valuable insights as well as a prototype
road feature extraction tool-set, for both national road authorities
and survey companies. These findings and knowledge contribute to a more
rapid, cost-effective and comprehensive approach to surveying road networks
which, in turn, enables a more efficient, comfortable and safer journey for all
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