Road rutting caused by vehicle loading in the wheel path is a major form of asphalt pavement distress. Hydroplaning and loss of skid resistance are directly related to high road rutting severity. Periodical measurements of rut depth are crucial to maintenance and rehabilitation planning. In this study, we explored the feasibility of using point clouds gathered by Mobile LiDAR systems to measure the rut depth. These point clouds that are collected along roads are usually used for other purposes, namely asset inventory or topographic survey. Taking advantage of available clouds to identify rutting severity in critical pavement areas can result in considerable economic and time saving and thus, added value, when compared with specific expensive rut measuring systems. Four different strategies of cloud points aggregation are presented to create the cross-section of points. Such strategies were established to improve the precision of individual sensor measurements. Despite the 5 mm precision of the used system, it was possible to estimate rut depth values that were slightly inferior. The rut depth values obtained from each cross-section strategy were compared with the manual field measured values. The cross-sections based on averaged cloud points sensor profile aggregation was revealed to be the most suitable strategy to measure rut depth. Despite the fact that the study was specifically conducted to measure rut depth, the evaluation results show that the methodology can also be useful for other mobile LiDAR point clouds cross-sections applications.

The railway structures need constant monitoring and maintenance to ensure the train circulation safety. Quality information concerning the infrastructure geometry, namely the three-dimensional linear elements, are crucial for that processes. Along with this work, a method to automated extract three-dimensional linear elements from point clouds collected by terrestrial mobile LiDAR systems along railways is presented. The proposed method takes advantage of the stored cloud point's attributes as an alternative to complex geometric methods applied over the point's cloud coordinates. Based on the assumption that the linear elements to extract are roughly parallel to the rail tracks and therefore to the system trajectory, the stored scan angle value was used to restrict the number of cloud points that represents the linear elements. A simple algorithm is then applied to that restricted number of points to get the three-dimensional polylines geometry. The obtained values of completeness, correctness and quality, validate the use of the methodology for linear elements extraction from mobile LiDAR data gathered along railway environments.Infrastructures 2019, 4, 46 2 of 15 the need for pedestrian access to the railway, while gathering dense and precise information of the railway surrounding area. There are essentially two MLS installation solutions to collect the point clouds along the railway: mounted directly on the train or mounted on a car transported by the train. Despite the different advantages and disadvantages of those different solutions, there are no significant accuracy differences in the obtained cloud points [1].However, the MLS gathered point clouds are non-selective, i.e., they represent all of the elements in the sensor surrounding at a given moment, without any sort of classification. This makes it impossible to distinguish between points that represent the ground, vegetation, poles, rail tracks, persons or any other object within the sensor range. It is then the necessary method's implementation that allows an efficient classification of the points that represents a specific object among the millions of the cloud points.In last few years, the use of LiDAR data has progressively increased, and several studies have been published where this type of data are used for railway infrastructures monitoring, such as tunnels [2,3], railway gauge [1], railway bridges [4], rail surface monitoring and quality indexing [5] and rail surface defect detection [6]. Some studies have focused on extracting point elements from MLS data, like catenary systems [7], poles [8], trees [9] and traffic signals [10]. In [11], previous existing algorithms are adapted to extract several nonlinear objects from the railway environments, namely buildings [12], electric poles and vertical signals [13], trees [14] and ground points [15].Regarding linear elements extraction, the automatic extraction of railway tracks lines is clearly the most recurrent subject in the existing bibliography about the use of MLS point clouds in railway envir...

In last decades, Mobile Light Detection And Ranging (LiDAR) systems were revealed to be an efficient and reliable method to collect dense and precise point clouds. The challenge now faced by researchers is the automatic object extraction from those point clouds, such as the curb break lines, which are essential to road rehabilitation projects and autonomous driving. Throughout this work, an efficient method to extract road curb break lines from mobile LiDAR point clouds is presented. The proposed method was based on the system working principles instead of an algorithmic application over the cloud as a mass of points. The point cloud was decomposed in the original sensor scan profiles. Then, a GPS epoch versus trajectory distance was used to eliminate most non-ground points. Finally, through a vertical monotone chain decomposition, candidate point arrays were created and the curb break lines are formed. The proposed method was shown to be able to avoid the occlusion effect caused by undergrowth. The method allows for distinguishing between right and left curbs and works on curved curbs. Both top and bottom tridimensional break lines were extracted. When compared with a reference manual method, in the tested dataset, the proposed method allowed for a decrease in the curb break lines extraction time from 25 min to less than 30 s. The extraction method provided completeness and correctness rates above 95% and 97%, respectively, and a quality value higher than 93%.Typically, when obtained using photogrammetric methods or a field survey, the break line designs are time consuming.Mobile Light Detection And Ranging (LiDAR) is a widely disseminated technology that allows for gathering dense and precise point clouds. Initially installed on aerial platforms, the static and mobile terrestrial systems rapidly emerged, becoming a crucial source of georeferenced data. However, LiDAR is a non-selective technique, i.e., the georeferenced point clouds represent the sensor's surrounding physical reality at an acquisition moment, indiscriminately, with no classification of terrain, vegetation, buildings, or any other object within the system range. The automatic classification and object extraction from those point clouds became a noteworthy challenge for researchers.Since the initial LiDAR systems, numerous techniques for automatic break lines extraction have been presented. Brugelmann [6] shortly describes some methods for break lines extraction from airborne point clouds, testing an approach proposed by Forstner [7]. In that approach, all pixels with a significant noise homogeneity measure are denoted as potential edge pixels. The quadratic variation, used as a homogeneity measure, indicates the extent of the curvature. Although the resulting lines have a lower quality when compared with the photogrammetrically measured break lines, the author showed that automatic break line extraction from airborne laser data is feasible.Other approach based on two intersecting planes as fitted functions can extract some 3D lines, but they nee...

ABSTRACT:The digital terrain models (DTM) assume an essential role in all types of road maintenance, water supply and sanitation projects. The demand of such information is more significant in developing countries, where the lack of infrastructures is higher. In recent years, the use of Mobile LiDAR Systems (MLS) proved to be a very efficient technique in the acquisition of precise and dense point clouds. These point clouds can be a solution to obtain the data for the production of DTM in remote areas, due mainly to the safety, precision, speed of acquisition and the detail of the information gathered. However, the point clouds filtering and algorithms to separate "terrain points" from "no terrain points", quickly and consistently, remain a challenge that has caught the interest of researchers. This work presents a method to create the DTM from point clouds collected by MLS. The method is based in two interactive steps. The first step of the process allows reducing the cloud point to a set of points that represent the terrain's shape, being the distance between points inversely proportional to the terrain variation. The second step is based on the Delaunay triangulation of the points resulting from the first step. The achieved results encourage a wider use of this technology as a solution for large scale DTM production in remote areas.

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