Extraction of Road Lane Markings from Mobile LiDAR Data

Zeybek, Mustafa

doi:10.1177/0361198120981948

Cited by 18 publications

(11 citation statements)

References 53 publications

(77 reference statements)

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“…Some highway agencies utilize GNSS data along with various imaging technologies to determine pavement condition down to as fine a scale as needed [30][31][32]. In addition, to establish an inventory of roadway networks along with attributes, LiDAR, along with integrated GNSS and INS, is used on a mobile platform or a vehicle, known as a mobile LiDAR system, to capture roadway markings, assets, and cross-sections [33]. The same technology has been used to collect locations for pedestrian infrastructure [31], guardrails, culverts, and bridges [34].…”

Section: Transportation System Managementmentioning

confidence: 99%

The Role of GNSS-RTN in Transportation Applications

et al. 2022

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The Global Navigation Satellite System—Real-Time Network (GNSS-RTN) is a satellite-based positioning system using a network of ground receivers (also called continuously operating reference stations (CORSs)) and a central processing center that provides highly accurate location services to the users in real-time over a broader geographic region. Such systems can provide geospatial location data with centimeter-level accuracy anywhere within the network. Geospatial location services are not only used in measuring ground distances and mapping topography; they have also become vital in many other fields such as aerospace, aviation, natural disaster management, and agriculture, to name but a few. The innovative and multi-disciplinary applications of geospatial data drive technological advancement towards precise and accurate location services available in real-time. Although GNSS-RTN technology is currently utilized in a few industries such as precision farming, construction industry, and land surveying, the implications of precise real-time location services would be far-reaching and more critical to many advanced transportation applications. The GNSS-RTN technology is promising in meeting the needs of automation in most advanced transportation applications. This article presents an overview of the GNSS-RTN technology, its current applications in transportation-related fields, and a perspective on the future use of this technology in advanced transportation applications.

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Section: Transportation System Managementmentioning

confidence: 99%

The Role of GNSS-RTN in Transportation Applications

et al. 2022

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“…LiDAR has increased in use due to its ability to create accurate three-dimensional (3D) models of any surface within visual sight of the sensing unit. Many DOTs have adopted practical uses of LiDAR technology for highway construction-related applications, such as establishing the original ground and contours and locating utilities [8,9,10]. There are three primary LiDAR applications:…”

Section: Light Detection and Rangingmentioning

confidence: 99%

“…2) Mobile LiDAR -system attached to a mobile object such as a vehicle or heavy equipment, which uses LiDAR, inertial navigation systems, and global positioning system (GPS) to capture roadway markings, assets, and cross sections [10].…”

Section: Light Detection and Rangingmentioning

confidence: 99%

Implementation of Visualization and Modeling Technologies for Transportation Construction

Harper¹,

Tran²,

Jaselskis³

2021

J. Civ. Eng. Constr.

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State departments of transportation (DOTs) increasingly use visualization and modeling technologies for delivering transportation projects across the United States. Advanced and innovative technologies have the ability to improve various construction processes and tasks while making the construction process more efficient and productive. Visualization and modeling technologies, which include building information modeling for infrastructure, light detection and ranging, virtual reality, and augmented reality, are becoming more commonplace in transportation construction. Yet, the use of these technologies varies among state DOTs. The intent of this study is to investigate the use of visualization and modeling technologies for transportation construction. This study employed a triangulation research methodology including an extensive literature review, survey questionnaire of DOTs, and seven case studies. Results of the study show that 92% of state DOTs use visualization and modeling technologies for construction. Then, 81% of DOTs use visualization and modeling technologies for constructability reviews, 38% use them for documentation of as-builts and simulating bridge and structure construction, and 35% use them for quality management, inspections, and monitoring progress of work. The main barriers to using visualization and modeling technologies include legal concerns with using digital models as contract documents, incompatibilities in software and hardware between the DOTs and contracted parties, and the appropriate knowledge, skills, and abilities required to use visualization and modeling technologies for construction. The findings from this study provides valuable information for state DOTs to approach their implementation and use of visualization and modeling technologies for transportation construction delivery.

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“…Road surfaces can be manually extracted from the available data set for preparing data. e automated road surface extraction can be done based on the third component of the normal vector [20] or identify the buffer zone of the trajectory line [21]. For further analysis of cracks, the 3D point cloud can be projected to 2D images and applied for crack extraction [22].…”

Section: Introductionmentioning

confidence: 99%

Pavement Crack Extraction Method from Mobile Laser Scanning Point Cloud

Phan

Huynh

2022

Advances in Civil Engineering

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The automatic detection of cracks in the road surface is of great significance for maintaining the road surface to ensure the safety of moving vehicles. The Hokuyo UTM 30 LX 2D laser scanner in this study is used to observe the road surface containing two cracks and distress areas. As a result, dense point clouds are created. The road surface is automatically extracted from the point clouds based on the geometry of the two curb lines. The normal vector of the points is calculated based on the principal component analysis method. Points belonging to cracks and zones of distress are extracted from the intensity gradient and the inclination angle between the two normal vectors of the neighboring points and then converted to binary images. The crack edges are extracted based on the Sobel operator. Although salt-and-pepper spots due to crack points extraction using intensity gradient affect the definition of crack edges, especially small cracks, large cracks and distress areas are extracted clearly. Research results show that reflectance intensity and elevation variation combination lead to the efficiency of crack extraction and distress area.

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Extraction of Road Lane Markings from Mobile LiDAR Data

Cited by 18 publications

References 53 publications

The Role of GNSS-RTN in Transportation Applications

The Role of GNSS-RTN in Transportation Applications

Implementation of Visualization and Modeling Technologies for Transportation Construction

Pavement Crack Extraction Method from Mobile Laser Scanning Point Cloud

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