Automatic Road Inventory Using LiDAR

Landa, Jaromír; Procházka, David

doi:10.1016/s2212-5671(14)00356-6

Cited by 37 publications

(19 citation statements)

References 7 publications

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“…The laser crack measurement system (LCMS) [10] is the basis of many of these systems and relies on the use of high-performance lasers attached to a vehicle that measures the profile of the road, roughness and slope at a high resolution of 1 mm whilst producing 3D profiles of the pavement. There have also been the development of mobile laser-based systems and those employing light detection and ranging systems [11,12].…”

Section: Background Of Pavement Distress Detection Techniquesmentioning

confidence: 99%

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

2020

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Road pavement conditions have significant impacts on safety, travel times, costs, and environmental effects. It is the responsibility of road agencies to ensure these conditions are kept in an acceptable state. To this end, agencies are tasked with implementing pavement management systems (PMSs) which effectively allocate resources towards maintenance and rehabilitation. These systems, however, require accurate data. Currently, most agencies rely on manual distress surveys and as a result, there is significant research into quick and low-cost pavement distress identification methods. Recent proposals have included the use of structure-from-motion techniques based on datasets from unmanned aerial vehicles (UAVs) and cameras, producing accurate 3D models and associated point clouds. The challenge with these datasets is then identifying and describing distresses. This paper focuses on utilizing images of pavement distresses in the city of Palermo, Italy produced by mobile phone cameras. The work aims at assessing the accuracy of using mobile phones for these surveys and also identifying strategies to segment generated 3D imagery by considering the use of algorithms for 3D Image segmentation to detect shapes from point clouds to enable measurement of physical parameters and severity assessment. Case studies are considered for pavement distresses defined by the measurement of the area affected such as different types of cracking and depressions. The use of mobile phones and the identification of these patterns on the 3D models provide further steps towards low-cost data acquisition and analysis for a PMS.

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Section: Background Of Pavement Distress Detection Techniquesmentioning

confidence: 99%

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

2020

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“…The most recent systems are composed by one or more acquisition devices and post-processing applications for semiautomatic or automated data extraction procedures, based on computer vision and image processing algorithms [33][34][35][36].…”

Section: State Of Art In Distress Detectionmentioning

confidence: 99%

Pavement Distress Detection Methods: A Review

2018

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The road pavement conditions affect safety and comfort, traffic and travel times, vehicles operating cost, and emission levels. In order to optimize the road pavement management and guarantee satisfactory mobility conditions for all road users, the Pavement Management System (PMS) is an effective tool for the road manager. An effective PMS requires the availability of pavement distress data, the possibility of data maintenance and updating, in order to evaluate the best maintenance program. In the last decade, many researches have been focused on pavement distress detection, using a huge variety of technological solutions for both data collection and information extraction and qualification. This paper presents a literature review of data collection systems and processing approach aimed at the pavement condition evaluation. Both commercial solutions and research approaches have been included. The main goal is to draw a framework of the actual existing solutions, considering them from a different point of view in order to identify the most suitable for further research and technical improvement, while also considering the automated and semi-automated emerging technologies. An important attempt is to evaluate the aptness of the data collection and extraction to the type of distress, considering the distress detection, classification, and quantification phases of the procedure.

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“…Duffell and Rudrum (2005) discussed that a point cloud can be utilised to perform road inventory mapping, including any road-scene structure, road pavement, traffic signalling devices, etc. (Williams et al 2013, Landa andProchazka 2014). According to Landa and Prochazka (2014), mobile LiDAR data can be used for different types of road-scene object inventory, which can be subdivided into three types: road pavement, road surface structures (e.g.…”

Section: Geometric Accuracy Validationmentioning

confidence: 99%

“…(Williams et al 2013, Landa andProchazka 2014). According to Landa and Prochazka (2014), mobile LiDAR data can be used for different types of road-scene object inventory, which can be subdivided into three types: road pavement, road surface structures (e.g. road markings and road cracks), and pole-like objects (e.g.…”

Section: Geometric Accuracy Validationmentioning

confidence: 99%

Use of mobile LiDAR in road information inventory: a review

Guan

Cao

et al. 2016

International Journal of Image and Data Fusion

174

119

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Mobile LiDAR technology is currently one of the attractive topics in the fields of remote sensing and laser scanning. Mobile LiDAR enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced data, in the form of three-dimensional (3D) point clouds. This technology has been gaining popularity in the recognition of roads and road-scene objects. A thorough review of available literature is conducted to inform the advancements in mobile LiDAR technologies and their applications in road information inventory. The literature review starts with a brief overview of mobile LiDAR technology, including system components, direct geo-referencing, data error analysis and geometrical accuracy validation. Then, this review presents a more in-depth description of current mobile LiDAR studies on road information inventory, including the detection and extraction of road surfaces, small structures on the road surfaces and polelike objects. Finally, the challenges and future trends are discussed. Our review demonstrates the great potential of mobile LiDAR technology in road information inventory. ARTICLE HISTORY

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Automatic Road Inventory Using LiDAR

Cited by 37 publications

References 7 publications

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

Exploiting Low-Cost 3D Imagery for the Purposes of Detecting and Analyzing Pavement Distresses

Pavement Distress Detection Methods: A Review

Use of mobile LiDAR in road information inventory: a review

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