Abstract
The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%.
Highlights
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Kaartinen et al [15] used a permanent test field to test the performance of research-based MLS systems, and the results showed that the accuracy of the point cloud data depends on the accuracy of sensors
(2) Due to the small size of curbs, the information that can be used for curb detection is limited
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Mobile-based light detection and ranging (LiDAR) systems are increasingly considered useful tools for city management. A mobile-based LiDAR system includes a laser scanner, an inertial measurement unit (IMU), and global navigation satellite system (GNSS). Mobile-based LiDAR can be used to collect 3D spatial information in order to build information models for purposes such as 3D city modeling [1], road and street planning and maintenance, virtual geographic environment modeling, and location-based services. Mobile-based LiDAR is currently the most popular system for acquiring accurate
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