Vectorization of Urban MLS Point Clouds: A Sequential Approach Using Cross Sections

Abstract

Abstract. Dense point clouds acquired with a mobile laser scanning system (MLS) device become usual raw data for different surveyor applications: topographic maps, 3D models, road inventories, risk assessment of vegetation on road or railroads. Thanks to important evolutions in technologies, MLS devices became powerful and very popular. In the meantime, the need for point cloud automatic processing tools is growing. However, the available tools have not yet reached a sufficient level of maturity. Using MLS point clouds to produce topographic maps, BIM model or other deliverables, requires very often manual vectorization (or digitalization) work. In the road context, the transition from point cloud to road map that consists in delineating curb or road edges, road markings, pole, trees, facades etc. is currently performed manually. To reduce these time-consuming operations, several solutions have been proposed in the literature. In this paper we present the first results of a method consisting in vectorizing urban point cloud scene. The originality of this work is to propose a global approach aiming to detect and vectorize simultaneously multiple objects. The developed algorithm uses cross-section analysis to detect road curbs and vertical objects. The first results are promising, since an F-score higher than 80% has been reached, even before applying road logic rules or additional knowledge. The detection and extraction of vertical objects including facades, trees, and poles, is more challenging but the detections also present a recall greater than 85%.

Vectorizing urban road markings from mobile laser scanning point clouds

“…However, the required information to identify the scan-lines is not always available. That's why, other techniques have been proposed to apply a profile-based road detection [12,13]. Scan-line-based or profile-based segmentation is often more specific than general tools designed for Digital Terrain Models (DTM) because they can only select the roadway if there is a curb.…”

Extraction of Road Markings From MLS Data: A Review

“…Yang et al (2020) exploit a pseudo scan-line structure to detect the curb positions using a sliding window. In a previous work, we used a sigmoid adjustment to detect and vectorize curbs Barçon et al (2022). In these articles, the road area is deduced from the curb detection results.…”

“…The improvement needed, can also maybe comes from exogeneous data. Barçon et al (2022) used national vector road database as prior knowledge for road orientation for instance.…”