Robust Approach for Urban Road Surface Extraction Using Mobile Laser Scanning 3d Point Clouds

Abstract

Abstract. Road surface extraction is crucial for 3D city analysis. Mobile laser scanning (MLS) is the most appropriate data acquisition system for the road environment because of its efficient vehicle-based on-road scanning opportunity. Many methods are available for road pavement, curb and roadside way extraction. Most of them use classical approaches that do not mitigate problems caused by the presence of noise and outliers. In practice, however, laser scanning point clouds are not free from noise and outliers, and it is apparent that the presence of a very small portion of outliers and noise can produce unreliable and non-robust results. A road surface usually consists of three key parts: road pavement, curb and roadside way. This paper investigates the problem of road surface extraction in the presence of noise and outliers, and proposes a robust algorithm for road pavement, curb, road divider/islands, and roadside way extraction using MLS point clouds. The proposed algorithm employs robust statistical approaches to remove the consequences of the presence of noise and outliers. It consists of five sequential steps for road ground and non-ground surface separation, and road related components determination. Demonstration on two different MLS data sets shows that the new algorithm is efficient for road surface extraction and for classifying road pavement, curb, road divider/island and roadside way. The success can be rated in one experiment in this paper, where we extract curb points; the results achieve 97.28%, 100% and 0.986 of precision, recall and Matthews correlation coefficient, respectively.

Detection and Segmentation of Pole-Like Objects in Mobile Laser Scanning Point Clouds

“…This paper focuses on pole-like objects (PLOs). Detection, delineation and segmentation of PLOs located in a road environment have great importance in roadway inventory (Chen et al, 2022), high density (HD) map generation (Plachetka et al, 2021), city modelling, urban planning, road infrastructure monitoring (Ha and Chaisomphob, 2020), intelligent transportation (Wang et al, 2021;Nurunnabi et al, 2022), traffic management (Tang et al, 2020;Li &Cheng., 2022), and most highly road safety inspection applications, as well as averting roadside accidents (Cabo et al, 2014;Wang et al, 2021). Image and video data are common to use for PLOs detection (Zhang et al, 2018;Sheweta et al, 2022).…”