Abstract
Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data.
Highlights
Light Detection And Ranging (LiDAR) enables 3D modelling of the real-world environment by measuring the time of return of emitted light pulses
The snake curve was initialised near the road feature using polyline vector data, while the external energy terms were derived from intensity, elevation and surface roughness-based images generated from the LiDAR data
The active contours have been extensively used to extract various features from high resolution digital images; they have been limitedly explored for extracting road features from LiDAR point cloud
Summary
Light Detection And Ranging (LiDAR) enables 3D modelling of the real-world environment by measuring the time of return of emitted light pulses. The methods developed for segmenting LiDAR data are based on the identification of planar or smooth surfaces and the classification of the point cloud based on its attributes [7] Based on these methods, several approaches have been attempted over the past decade to extract road and its associated features from Airborne Laser Scanning (ALS) and MLS datasets. LiDAR intensity and pulse width attributes can be a useful source of information for extracting edges, which need to be thoroughly explored In this context, active contour models present a robust segmentation approach that makes efficient use of specific information available about objects in the input data rather than processing all of the data [14].
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