Mobile Laser Scanning Data for the Evaluation of Pavement Surface Distress

Abstract

The surface conditions of road pavements, including the occurrence and severity of distresses present on the surface, are an important indicator of pavement performance. Periodic monitoring and condition assessment is an essential requirement for the safety of vehicles moving on that road and the wellbeing of people. The traditional characterization of the different types of distress often involves complex activities, sometimes inefficient and risky, as they interfere with road traffic. The mobile laser systems (MLS) are now widely used to acquire detailed information about the road surface in terms of a three-dimensional point cloud. Despite its increasing use, there are still no standards for the acquisition and processing of the data collected. The aim of our work was to develop a procedure for processing the data acquired by MLS, in order to identify the localized degradations that mostly affect safety. We have studied the data flow and implemented several processing algorithms to identify and quantify a few types of distresses, namely potholes and swells/shoves, starting from very dense point clouds. We have implemented data processing in four steps: (i) editing of the point cloud to extract only the points belonging to the road surface, (ii) determination of the road roughness as deviation in height of every single point of the cloud with respect to the modeled road surface, (iii) segmentation of the distress (iv) computation of the main geometric parameters of the distress in order to classify it by severity levels. The results obtained by the proposed methodology are promising. The procedures implemented have made it possible to correctly segmented and identify the types of distress to be analyzed, in accordance with the on-site inspections. The tests carried out have shown that the choice of the values of some parameters to give as input to the software is not trivial: the choice of some of them is based on considerations related to the nature of the data, for others, it derives from the distress to be segmented. Due to the different possible configurations of the various distresses it is better to choose these parameters according to the boundary conditions and not to impose default values. The test involved a 100-m long urban road segment, the surface of which was measured with an MLS installed on a vehicle that traveled the road at 10 km/h.

A Method for Obtaining a DEM with Curved Abscissa from MLS Data for Linear Infrastructure Survey Design

“…Before continuing with the processing of the data derived from the MLS survey, the point cloud should be edited since it is made up of the points that shape all of the elements on the road scene; they can either belong to the road surface (GP, ground points) or not belong to it (NGP, no ground points). The extraction of the bare road surface (made of GPs only) and the removal of the outliers was carried out by applying the method introduced in [37]. The procedure is based on the MSAC (M-estimator Sample Consensus) method.…”

“…Horizontal signage extraction from point cloud and roadway axis vectorization [39]; • Extraction of MLS trajectory [37], in the case of a single-lane analysis and only if the vehicle has traveled along the section under consideration without performing evasive maneuvers (e.g., overtaking) or other types of maneuvers that compromise the condition of parallelism between MLS trajectory.…”

Machine Learning Clustering for Point Clouds Optimisation via Feature Analysis in Cultural Heritage

“…In the literature, UAV systems used for photogrammetric purposes are usually applied for three-dimensional surveys of complex structures in the Cultural Heritage field. Compared with TLS -Terrestrial Laser Scanning (De Blasiis et al, 2020), the main factors in favour of using drones for acquisition are: lower instrument costs, faster field data collection, and first of all, better ratios 3D model necessary for correct analysis of colour results and archaeological identification (Rinaudo et al, 2012;Barba et al, 2019). Especially in the field of Cultural Heritage (CH), the applications of photogrammetry for different purposes are numerous (Themistocleous et al, 2015).…”

Assessing of the Road Pavement Roughness by Means of LiDAR Technology

“…One way they can increase productivity is through the use of new technologies. These include LiDAR (light detection and ranging) technology, which is currently one of the most interesting remote sensing techniques for infrastructure applications [22][23][24][25]. Laser scanners (LS), which are based on this technology, provide very accurate and high-resolution 3D data through dense point clouds.…”