Rapid Inspection of Pavement Markings Using Mobile Lidar Point Clouds

Zhang, Haocheng; Li, Jonathan; Cheng, Ming; Wang, Cheng

doi:10.5194/isprs-archives-xli-b1-717-2016

Cited by 9 publications

(6 citation statements)

References 5 publications

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“…Main application of LiDARs in road perception is related with detecting the ground plane and road limits [68], as well as detecting obstacles that could occlude parts of the road. In recent works, LiDAR based solutions also take advantage of the higher reflectivity of road marks with respect to the pavement (gray and black material) to detect lane [69,70] and pavement markers [71]. Poor road maintenance can affect markers reflectivity to the point of making them undetectable by LiDAR.…”

Section: ) Lidar Based Solutionsmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

214

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After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show the intention of the market in sensors technologies for Automated Vehicles.

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Section: ) Lidar Based Solutionsmentioning

confidence: 99%

A Review of Sensor Technologies for Perception in Automated Driving

Martí

Miguel

Fernández

et al. 2019

IEEE Intell. Transport. Syst. Mag.

214

View full text Add to dashboard Cite

show abstract

“…Similarly, Guan et al (2014) assumed a Gaussian normal distribution of the intensity across the road surface to support the following road marking extraction. Zhang et al (2016) developed an intensity correction based on a linear regression of the cosine of the scan angle rank versus intensity to improve its consistency as a relative measure for comparing road markings. Their primary focus in developing this correlation is to utilize this information for improving the road marking extraction algorithm results.…”

Section: Overviewmentioning

confidence: 99%

“…While few studies have focused on using lidar data for pavement marking retroreflectivity evaluation, several studies have utilized intensity information for road marking extraction (e.g., Guan et al 2016, Zhang et al 2016, Jung et al 2019 or to serve as a reference for improved geo-referencing (e.g., Toth et al 2008).…”

Section: Overviewmentioning

confidence: 99%

Pavement Marking Reflectivity Evaluation Through Radiometric Calibration of the Leica P40 Terrestrial Laser Scanner

Che

Parrish

Jung

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Pavement markings serve as important traffic control devices, delineating traffic lanes and conveying regulations, guidance or warnings to roadway users. To ensure that pavement markings are clearly visible, especially at night, transportation agencies periodically assess the retroreflectivity of various categories of markings through manual approaches at discrete location. Because the radiometric information such as intensity in the lidar data cannot inherently be considered as a retroreflectivity measurement without additional processing, this study rigorously assesses the ability to determine pavement marking retroreflectivity from the Leica ScanStation P40 through radiometric calibration. For the evaluation, data were collected at a study site in Philomath, Oregon using the Leica P40, Leica Pegasus: Two mobile lidar system, and a handheld retroreflectometer as a reference. The results show that, with appropriate calibration, the lidar data can adequately assess the retroreflectivity of pavement markings. Additionally, while corrections have been proposed for range and angle of incidence, these corrections are not straightforward to apply for retroreflective materials, as will be discussed herein. While mobile lidar technology is ideal for a system-wide asset management framework, terrestrial laser scanning can be utilized for detailed investigations at sites such as intersections with highly variable wear where both can enable significant cost savings and applied for a variety of purposes simultaneously including asset management and project development.</p>

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“…The demand for maps for autonomous driving has recently increased. Therefore, point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects (Zhang et al, 2016). The segmentation of MLS point clouds is required for the extraction of road information.…”

Section: Introductionmentioning

confidence: 99%

MLS Point Cloud Segmentation Based on Feature Points of Scanlines

Honma¹,

Date

Kanai

2019

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

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Point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects. In this paper, we present a scanline-based MLS point cloud segmentation method for various road and road side objects. First, end points of the scanline, jump edge points, and corner points are extracted as feature points. The feature points are then interpolated to accurately extract irregular parts consisting of irregularly distributed points such as vegetation. Next, using a point reduction method, additional feature points on a smooth surface are extracted for segmentation at the edges of the curb cut. Finally, points between the feature points are extracted as flat segments on the scanline, and continuing feature points are extracted as irregular segments on the scanline. Furthermore, these segments on the scanline are integrated as flat or irregular regions. In the extraction of the feature points, neighboring points based on the spatial distance are used to avoid being influenced by the difference in the point density. Based on experiments, the effectiveness of the proposed method was indicated based on an application to an MLS point cloud.

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Rapid Inspection of Pavement Markings Using Mobile Lidar Point Clouds

Cited by 9 publications

References 5 publications

A Review of Sensor Technologies for Perception in Automated Driving

A Review of Sensor Technologies for Perception in Automated Driving

Pavement Marking Reflectivity Evaluation Through Radiometric Calibration of the Leica P40 Terrestrial Laser Scanner

MLS Point Cloud Segmentation Based on Feature Points of Scanlines

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