An automated algorithm for extracting road edges from terrestrial mobile LiDAR data |
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| Affiliation: | 1. Department of Natural Resources and Environmental Engineering, School of Mining Engineering, University of Vigo, 36310 Vigo, Spain;2. Department of Materials Engineering, Applied Mechanics and Construction, School of Industrial Engineering, University of Vigo, 36310, Spain;1. Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Canada;2. Department of Civil Engineering, Central Tehran Branch, Islamic Azad University (IAUCTB), Emam Hasan Blvd., Ashrafi Esfahani Highway, District 2, Tehran, Iran;3. Highway Safety Research Center, University of North Carolina, 730 Martin Luther King Jr Blvd., Chapel Hill, NC 27514, USA;1. Geographic Information System (GIS) Cell, Motilal Nehru National Institute of Technology Allahabad, Allahabad 211004, Uttar Pradesh, India;2. Department of Civil Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad 211004, Uttar Pradesh, India;3. Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, Uttar Pradesh, India;1. Université Paris-Est, IGN, MATIS, France;2. NASA – Jet Propulsion Laboratory, USA;3. R&D Unit INESC Coimbra, Portugal;4. Bordeaux INP, EA 4592, France |
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| Abstract: | Terrestrial mobile laser scanning systems provide rapid and cost effective 3D point cloud data which can be used for extracting features such as the road edge along a route corridor. This information can assist road authorities in carrying out safety risk assessment studies along road networks. The knowledge of the road edge is also a prerequisite for the automatic estimation of most other road features. In this paper, we present an algorithm which has been developed for extracting left and right road edges from terrestrial mobile LiDAR data. The algorithm is based on a novel combination of two modified versions of the parametric active contour or snake model. The parameters involved in the algorithm are selected empirically and are fixed for all the road sections. We have developed a novel way of initialising the snake model based on the navigation information obtained from the mobile mapping vehicle. We tested our algorithm on different types of road sections representing rural, urban and national primary road sections. The successful extraction of road edges from these multiple road section environments validates our algorithm. These findings and knowledge provide valuable insights as well as a prototype road edge extraction tool-set, for both national road authorities and survey companies. |
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| Keywords: | Edge Automation Extraction LiDAR Terrestrial mobile |
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