Using mobile laser scanning data for automated extraction of road markings

Guan, Haiyan; Li, Jonathan; Yu, Yongtao; Wang, Cheng; Chapman, Michael A.; Wang, Jinhu

doi:10.1016/j.isprsjprs.2013.11.005

Cited by 228 publications

(163 citation statements)

References 12 publications

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“…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. In contrast, Zhao et al [28], Smadja et al [29] and Guan et al [35] show methods for automatically identifying road points from MLS data, but their performance is not assessed. Boyko and Funkhouser, and Bin et al [26,30] are focused on the detection of the road surface, but limited to the presence of curbs.…”

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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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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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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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“…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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“…Recognizing and extracting features from MLS point clouds has been discussed by e.g. Pu et al (2011), Yang et al (2013 and Guan et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Initial Tests and Accuracy Assesment of a Compact Mobile Laser Scanning System

Julge

Ellmann

Vajakas

et al. 2016

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

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ABSTRACT:Mobile laser scanning (MLS) is a faster and cost-effective alternative to static laser scanning, even though there is a slight trade-off in accuracy. This contribution describes a compact mobile laser scanning system mounted on a vehicle. The technical parameters of the used system components, i.e. a small LIDAR sensor Velodyne VLP-16 and a dual antenna GNSS/INS system Advanced Navigation Spatial Dual, are reviewed, along with the integration of these components for spatial data acquisition. Calculation principles of 3D coordinates from the real-time data of all the involved sensors are discussed. The field tests were carried out in a controlled environment of a parking lot and at different velocities. Experiments were carried out to test the ability of the GNSS/INS system to cope with difficult conditions, e.g. sudden movements due to cornering or swerving. The accuracy of the resulting MLS point cloud is evaluated with respect to high-accuracy static terrestrial laser scanning data. Problems regarding combining LIDAR, GNSS and INS sensors are outlined, as well as the initial accuracy assessments. Initial tests revealed errors related to insufficient quality of inertial data and a need for the trajectory post-processing calculations. Although this study was carried out while the system was mounted on a car, there is potential for operating the system on an unmanned aerial vehicle, all-terrain vehicle or in a backpack mode due to its relatively compact size.

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Using mobile laser scanning data for automated extraction of road markings

Cited by 228 publications

References 12 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

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

Initial Tests and Accuracy Assesment of a Compact Mobile Laser Scanning System

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