MLS Point Cloud Segmentation Based on Feature Points of Scanlines

Abstract

Point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects. In this paper, we present a scanline-based MLS point cloud segmentation method for various road and road side objects. First, end points of the scanline, jump edge points, and corner points are extracted as feature points. The feature points are then interpolated to accurately extract irregular parts consisting of irregularly distributed points such as vegetation. Next, using a point reduction method, additional feature points on a smooth surface are extracted for segmentation at the edges of the curb cut. Finally, points between the feature points are extracted as flat segments on the scanline, and continuing feature points are extracted as irregular segments on the scanline. Furthermore, these segments on the scanline are integrated as flat or irregular regions. In the extraction of the feature points, neighboring points based on the spatial distance are used to avoid being influenced by the difference in the point density. Based on experiments, the effectiveness of the proposed method was indicated based on an application to an MLS point cloud.

Extraction of Urban Road Boundary Points from Mobile Laser Scanning Data Based on Cuboid Voxel

“…The algorithm was performed by Python programming language on the PyCharm2021 platform, and the point cloud data were displayed, measured, and evaluated in CloudCompare2.13 software. To verify the effectiveness of the proposed method, a comparative experiment was performed with the methods proposed in References [43][44][45], and the results of different algorithms were quantitatively evaluated using three performance metrics used in Reference [20], namely, precision, recall, and quality.…”

“…To further verify the performance of this method, the curb point extraction results of the proposed method were compared with those of the methods proposed in References [43][44][45]. Data 2 shown in Figure 9 was selected for comparison experiments.…”

“…The data contained complex scenes, such as fences, border trees, and vehicle occlusion. The data could regularize scanning lines, providing basic data support for Reference [43]. The method proposed in Reference [43] separates the point cloud into scan lines and extracts the road curb points by detecting corner points, which have a large bending angle.…”

“…The data could regularize scanning lines, providing basic data support for Reference [43]. The method proposed in Reference [43] separates the point cloud into scan lines and extracts the road curb points by detecting corner points, which have a large bending angle. This method exhibits high calculation accuracy and efficiency in a simple road environment, but its performance is not ideal for the data in this paper.…”

“…This method exhibits high calculation accuracy and efficiency in a simple road environment, but its performance is not ideal for the data in this paper. Due to the similar geometric features of roadside fences and curbs, the method in Reference [43] failed to effectively separate the two, resulting in a large number of pseudo curb points. The accuracy of curb point extraction was reduced due to the irregular distribution of points and the poor extraction of scanning lines, so the accurate extraction of scanning lines is the key to this method.…”