Automated Extraction of Road Markings from Mobile Lidar Point Clouds

Wang, Jinhu; Fang, Lina; Li, Qingquan; Li, Jonathan

doi:10.14358/pers.78.4.331

Cited by 138 publications

(81 citation statements)

References 8 publications

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“…The road marking points were clustered and convex hulls were fitted to them. Yang et al (2012) described an automated approach for extracting road markings from MLS data. In their approach, 2D image was generated from LiDAR point cloud data and then road markings were filtered by applying threshold to the LiDAR intensity and elevation values.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

107

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a b s t r a c tRoad markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Automated road markings extraction from mobile laser scanning data

Kumar

McElhinney

Lewis

et al. 2014

International Journal of Applied Earth Observation and Geoinfor

107

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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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“…Toth et al (2008) selected an intensity value based on the intensity distribution in a search window as a global threshold for the extraction. Yang et al (2012) extracted continuous edge lines and broken lane line markings successfully. Studies were also undertaken aiming at solving the problem caused by inconstant intensity.…”

Section: Introductionmentioning

confidence: 99%

Rapid Inspection of Pavement Markings Using Mobile Lidar Point Clouds

Zhang

Cheng

et al. 2016

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

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ABSTRACT:This study aims at building a robust semi-automated pavement marking extraction workflow based on the use of mobile LiDAR point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the mobile LiDAR point clouds are converted into the radiometrically corrected intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu's thresholding, neighborcounting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the mobile LiDAR dataset acquired in Xiamen (Fujian, China) with different road environments by the RIEGL VMX-450 system. The results demonstrated that the proposed workflow and our software tool can achieve 93% in completeness, 95% in correctness, and 94% in F-score when using Xiamen dataset.

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Automated Extraction of Road Markings from Mobile Lidar Point Clouds

Cited by 138 publications

References 8 publications

Automated road markings extraction from mobile laser scanning data

Automated road markings extraction from mobile laser scanning data

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

Rapid Inspection of Pavement Markings Using Mobile Lidar Point Clouds

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