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

Cabo, Carlos; Kukko, Antero; García-Cortés, Silverio; Kaartinen, Harri; Hyyppä, Juha; Ordóñez, Celestino

doi:10.3390/rs8090740

Cited by 32 publications

(23 citation statements)

References 28 publications

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“…The combination of such data with ALS data sets allows automated 3D modeling and keeping maps updated. Algorithms have been developed for extracting road markings [57], curbs [80], road edges [81], and pole-like objects [82] from MLS data. For dense MLS data sets, methods for automatic identification and segmentation of various urban furniture have been developed [83,84].…”

Section: Built and Road Environmentsmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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Topographic databases maintained by national mapping agencies are currently the most common nationwide data sets in geo-information. The application of laser scanning as source data for surveying is increasing. Along with this development, several analysis methods that utilize dense point clouds have been introduced. We present the concept of producing a dense nationwide point cloud, produced from multiple sensors and containing multispectral information, as the national core data for geo-information. Geo-information products, such as digital terrain and elevation models and 3D building models, are produced automatically from these data. We outline the data acquisition, processing, and application of the point cloud. As a national data set, a dense multispectral point cloud could produce significant cost savings via improved automation in mapping and a reduction of overlapping surveying efforts.

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Section: Built and Road Environmentsmentioning

confidence: 99%

Nationwide Point Cloud—The Future Topographic Core Data

Virtanen

Kukko

Kaartinen

et al. 2017

IJGI

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“…Various studies were carried out in the past years (Zai et al, 2018, Soilán et al, 2017. Nevertheless, the prior knowledge about LiDAR-derived control point selection makes the accurate driving line generation very challenging (Cabo et al, 2016). Therefore, developing an efficient and robust algorithm to generate driving lines while precisely recording their geometry and coordinate information from large-scale and unordered MLS point clouds, has been essentially needed.…”

Section: Introductionmentioning

confidence: 99%

Automated Extraction of Driving Lines from Mobile Laser Scanning Point Clouds

et al. 2019

Adv. Cartogr. GIScience Int. Cartogr. Assoc.

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<p><strong>Abstract.</strong> This paper presents a novel approach to automated generation of driving lines from mobile laser scanning (MLS) point cloud data. The proposed method consists of three steps: road surface segmentation, road marking extraction and classification, and driving line generation. The voxel-based upward-growing algorithm was firstly used to extract ground points from the raw MLS point clouds followed by segmentation of road surface using a region-growing algorithm. Then, the statistical outlier removal filter was applied to separate and refine the road marking points followed by extracting and classifying the lane markings based on the geometric features of different road markings using empirical hierarchical decision trees. Finally, land node structures were constructed followed by generation of driving lines using a curve-fitting algorithm. The proposed method was tested on both circular road sections and irregular intersections. The smoothing spline curve fitting model was tested on the circular road sections, while the Catmull-Rom spline with five control points was used to generate the driving lines at road intersections. The overall performance of the proposed algorithms is promising with 96.0% recall, 100.0% precision, and 98.0% F1-score for the lane marking extraction specifically. Most significantly, the validation results demonstrate that the driving lines can be effectively generated within 20&thinsp;cm-level localization accuracy at an average of 3.5% miscoding using MLS point clouds, which meets the requirement of localization accuracy of fully autonomous driving functions. The results demonstrate the proposed methods can successfully generate road driving lines in the test datasets to support the development of high-definition maps.</p>

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“…More recently, a cost-effective traffic sign inventory method was proposed in Ai and Tsai, (2015). In parallel, Cabo et al, (2016) applied an automatic algorithm to detect road asphalt edge limits for road maintenance and safety assessment. An adequate review of the scientific literature can be found in Yang et al, (2013).…”

Section: Introductionmentioning

confidence: 99%

Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

Martín-Jiménez

Zazo

Justel

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

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Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as "ground truth."

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

Cited by 32 publications

References 28 publications

Nationwide Point Cloud—The Future Topographic Core Data

Nationwide Point Cloud—The Future Topographic Core Data

Automated Extraction of Driving Lines from Mobile Laser Scanning Point Clouds

Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

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