Automated road markings extraction from mobile laser scanning data

Kumar, Pankaj; McElhinney, Conor P.; Lewis, Paul; McCarthy, Tim

doi:10.1016/j.jag.2014.03.023

Cited by 103 publications

(74 citation statements)

References 13 publications

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“…For instance, some studies [6,24,31] focus on the extraction of road markings and, even though some of them report their performance in terms of completeness and correctness, these values are not comparable to the ones obtained in this study, as they do not try to detect the road edge. Several other studies present algorithms for automatic detection of curbs [15,22,23,25,32,34], and most of them assess completeness and correctness, but these refer to the length of the curbs detected.…”

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confidence: 78%

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An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

et al. 2016

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Accurate road asphalt extent delineation is needed for road and street planning, road maintenance, and road safety assessment. In this article, a new approach for automatic roadside delineation is developed based on the line clouds concept. The method relies on line cloud grouping from point cloud laser data. Using geometric criteria, the initial 3D LiDAR point data is structured in lines covering the road surface. These lines are then grouped according to a set of quasi-planar restriction rules. Road asphalt edge limits are extracted from the end points of lines belonging to these groups. Finally a two-stage smoothing procedure is applied to correct for edge occlusions and other anomalies. The method was tested on a 2.1 km stretch of road, and the results were checked using a RTK-GNSS measured dataset as ground truth. Correctness and completeness were 99% and 97%, respectively.

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contrasting

confidence: 78%

“…Following a similar procedure to the one described in [31], the lines are grouped based on their proximity and similarities. It is assumed that the lines from consecutive sweeps on planar or some ruled surfaces are almost parallel and, frequently, their initial or end nodes are close to each other.…”

Section: Line Groupingmentioning

confidence: 99%

An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

et al. 2016

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“…Intensity, which is insensitive to ambient light and shadowing [5], is initially used to improve point cloud separability. Apart from visualization purposes, intensity data can be used as a major or complementary data source in various studies, such as vegetation and forest investigation [6,7], road traffic marking identification [8,9], water content extraction [6,[10][11][12][13], metro tunnel inspection [10,14], and lithological differentiation [5,15,16].…”

Section: Introductionmentioning

confidence: 99%

Specular Reflection Effects Elimination in Terrestrial Laser Scanning Intensity Data Using Phong Model

Tan

Cheng

2017

Remote Sensing

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Abstract:The intensity value recorded by terrestrial laser scanning (TLS) systems is significantly influenced by the incidence angle. The incidence angle effect is an object property, which is mainly related to target scattering properties, surface structures, and even some instrumental effects. Most existing models focus on diffuse reflections of rough surfaces and ignore specular reflections, despite that both reflections simultaneously exist in all natural surfaces. Due to the coincidence of the emitter and receiver in TLS, specular reflections can be ignored at large incidence angles. On the contrary, at small incidence angles, TLS detectors can receive a portion of specular reflections. The received specular reflections can trigger highlight phenomenon (hot-spot effects) in the intensity data of the scanned targets, particularly those with a relatively smooth or highly-reflective surface. In this study, a new method that takes diffuse and specular reflections, as well as the instrumental effects into consideration, is proposed to eliminate the specular reflection effects in TLS intensity data. Diffuse reflections and instrumental effects are modeled by a polynomial based on Lambertian reference targets, whereas specular reflections are modeled by the Phong model. The proposed method is tested and validated on different targets scanned by the Faro Focus 3D 120 terrestrial scanner. Results imply that the coefficient of variation of the intensity data from a homogeneous surface is reduced by approximately 38% when specular reflections are considered. Compared with existing methods, the proposed method exhibits good feasibility and high accuracy in eliminating the specular reflection effects for intensity image interpretation and 3D point cloud representation by intensity.

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“…Chen (2009) used a single global thresholding to detect road markings, which severely suffers from the range effect of intensity, resulting in inaccuracies. To overcome this problem, local intensity thresholding method (Guan and Li, 2014;Yu and Li, 2015) and range dependent thresholding method (Yang and Fang, 2012;Kumar and Mcelhinney, 2014) are proposed. Local intensity thresholding uses different thresholds in different segments of road surface but it is hard to determine the size of locality in which a uniform threshold can separate road marking from road surface.…”

Section: Studies On Road Surface Features Extractionmentioning

confidence: 99%

Using Mobile Laser Scanning Data for Features Extraction of High Accuracy Driving Maps

Wang

Liu

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

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Automated road markings extraction from mobile laser scanning data

Cited by 103 publications

References 13 publications

An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

An Algorithm for Automatic Road Asphalt Edge Delineation from Mobile Laser Scanner Data Using the Line Clouds Concept

Specular Reflection Effects Elimination in Terrestrial Laser Scanning Intensity Data Using Phong Model

Using Mobile Laser Scanning Data for Features Extraction of High Accuracy Driving Maps

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