Mobile Mapping Technology (MMT) is one of the most important 3D spatial data acquisition technologies. The state-of-the-art mobile mapping systems, equipped with laser scanners and named Mobile LiDAR Scanning (MLS) systems, have been widely used in a variety of areas, especially in road mapping and road inventory. With the commercialization of Advanced Driving Assistance Systems (ADASs) and self-driving technology, there will be a great demand for lane-level detailed 3D maps, and MLS is the most promising technology to generate such lane-level detailed 3D maps. Road markings and road edges are necessary information in creating such lane-level detailed 3D maps. This paper proposes a scan line based method to extract road markings from mobile LiDAR point clouds in three steps: (1) preprocessing; (2) road points extraction; (3) road markings extraction and refinement. In preprocessing step, the isolated LiDAR points in the air are removed from the LiDAR point clouds and the point clouds are organized into scan lines. In the road points extraction step, seed road points are first extracted by Height Difference (HD) between trajectory data and road surface, then full road points are extracted from the point clouds by moving least squares line fitting. In the road markings extraction and refinement step, the intensity values of road points in a scan line are first smoothed by a dynamic window median filter to suppress intensity noises, then road markings are extracted by Edge Detection and Edge Constraint (EDEC) method, and the Fake Road Marking Points (FRMPs) are eliminated from the detected road markings by segment and dimensionality feature-based refinement. The performance of the proposed method is evaluated by three data samples and the experiment results indicate that road points are well extracted from MLS data and road markings are well extracted from road points by the applied method. A quantitative study shows that the proposed method achieves an average completeness, correctness, and F-measure of 0.96, 0.93, and 0.94, respectively. The time complexity analysis shows that the scan line based road markings extraction method proposed in this paper provides a promising alternative for offline road markings extraction from MLS data.

Point cloud filtering is an essential preprocessing step in 3D (three-dimensional) LiDAR (light detection and ranging) point cloud processing. The filtering of mobile LiDAR scanning point clouds is much more challenging due to their non-uniform distribution, the large-scale of missing data areas and the existence of both large size objects and small land features. This paper proposes a new filtering method that combines range constraint, slope constraint and angular position constraint to filter ground surface points from mobile LiDAR point clouds. Firstly, a cylindrical coordinate system (CCS) is established for each block of mobile LiDAR point clouds and three attributes of mobile LiDAR points, i.e., the angular position attribute (AA), longitudinal distance attribute (LA) and range attribute (RA), are computed. Then, the mobile LiDAR point clouds are structured into a grid according to the AA and LA. Finally, the point clouds are filtered by a single cross-section filter (SCSF) using range constraint and slope constraint, followed by a multiple cross-section filter (MCSF) using range constraint and angular position constraint. Five datasets are used to validate the proposed method. The experimental results show that the proposed new filtering method achieves an average type I error, type II error, and total error of 1.426%, 1.885%, and 1.622%, respectively.