Semi-automated extraction and delineation of 3D roads of street scene from mobile laser scanning point clouds |
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| Institution: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;2. Department of Geography & Environmental Management, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1;1. Université Paris-Est, IGN, MATIS, 73 Av. de Paris, 94160 Saint-Mandé, France;2. Photogrammetry and Remote Sensing, ETH Zürich, Switzerland;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 |
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| Abstract: | Accurate 3D road information is important for applications such as road maintenance and virtual 3D modeling. Mobile laser scanning (MLS) is an efficient technique for capturing dense point clouds that can be used to construct detailed road models for large areas. This paper presents a method for extracting and delineating roads from large-scale MLS point clouds. The proposed method partitions MLS point clouds into a set of consecutive “scanning lines”, which each consists of a road cross section. A moving window operator is used to filter out non-ground points line by line, and curb points are detected based on curb patterns. The detected curb points are tracked and refined so that they are both globally consistent and locally similar. To evaluate the validity of the proposed method, experiments were conducted using two types of street-scene point clouds captured by Optech’s Lynx Mobile Mapper System. The completeness, correctness, and quality of the extracted roads are over 94.42%, 91.13%, and 91.3%, respectively, which proves the proposed method is a promising solution for extracting 3D roads from MLS point clouds. |
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