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Open AccessTechnical Note

Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations

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Departamento de Ingeniería del Transporte, Territorio y Urbanismo, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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Departamento de Ingeniería Geológica y Minera, Universidad Politécnica de Madrid, 28003 Madrid, Spain
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Departamento de Ingeniería y Gestión Forestal y Ambiental, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2730; https://doi.org/10.3390/rs11232730
Received: 29 October 2019 / Revised: 16 November 2019 / Accepted: 18 November 2019 / Published: 20 November 2019
(This article belongs to the Special Issue Lidar Remote Sensing in 3D Object Modelling)
Existing roads require periodic evaluation in order to ensure safe transportation. Estimations of the available sight distance (ASD) are fundamental to make sure motorists have sufficient visibility to perform basic driving tasks. Mobile LiDAR Systems (MLS) can provide these evaluations with accurate three-dimensional models of the road and surroundings. Similarly, Geographic Information System (GIS) tools have been employed to obtain ASD. Due to the fact that widespread GIS formats used to store digital surface models handle elevation as an attribute of location, the presented methodology has separated the representation of ground and aboveground elements. The road geometry and surrounding ground are stored in digital terrain models (DTM). Correspondingly, abutting vegetation, manmade structures, road assets and other roadside elements are stored in 3D objects and placed on top of the DTM. Both the DTM and 3D objects are accurately obtained from a denoised and classified LiDAR point cloud. Based on the consideration that roadside utilities and most manmade structures are well-defined geometric elements, some visual obstructions are extracted and/or replaced with 3D objects from online warehouses. Different evaluations carried out with this method highlight the tradeoff between the accuracy of the estimations, performance and geometric complexity as well as the benefits of the individual consideration of road assets. View Full-Text
Keywords: 3D point cloud; 3D objects; LiDAR models; sight distance; road safety 3D point cloud; 3D objects; LiDAR models; sight distance; road safety
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MDPI and ACS Style

González-Gómez, K.; Iglesias, L.; Rodríguez-Solano, R.; Castro, M. Framework for 3D Point Cloud Modelling Aimed at Road Sight Distance Estimations. Remote Sens. 2019, 11, 2730.

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