Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data

Wang, Hanyun; Luo, Haipeng; Wen, Chenglu; Cheng, Jun; Li, Peng; Chen, Yiping; Wang, Cheng; Li, Jonathan

doi:10.1109/lgrs.2015.2449074

Cited by 65 publications

(53 citation statements)

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“…The use of a properly normalized reflectance attribute along with a uniform and dense point cloud will provide an improved extraction of road edges in both the sections. Yang, Fang, and Li [21] and Wang et al [24] validated the kerb edges extracted from MLS data by detailing an average completeness values of 95.13% and 95.41%, respectively, while average correctness values of 97.04% and 99.35%, respectively. Guan, Li, Yu, Chapman, and Wang [23] reported an average horizontal and vertical root mean square error (RMSE) values of 0.08 and 0.02 m, respectively, for the kerb edges extracted from MLS data.…”

Section: Resultsmentioning

confidence: 99%

“…The road marking outputs were evaluated by making their comparison with manual interpretation, while the validation of kerb edges was reported in [23] based on estimating their Euclidean distance with reference ground points collected using total station. Wang et al [24] developed a method for detecting kerb edges from MLS point cloud in an urban environment. The point cloud data were partitioned into a number of overlapping data blocks based on vehicle trajectory, and then, salient points in each block were extracted by projecting the distance of each point's normal vector to the point cloud's dominant vector into a hyperbolic tangent function space.…”

Section: Literature Reviewmentioning

confidence: 99%

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Snake Energy Analysis and Result Validation for a Mobile Laser Scanning Data-Based Automated Road Edge Extraction Algorithm

Kumar

Lewis

McElhinney

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The negative impact of road accidents cannot be ignored in terms of the very sizeable social and economic loss. Road infrastructure has been identified as one of the main causes of the road accidents. They are required to be recorded, located, measured, and classified in order to schedule maintenance and identify the possible risk elements of the road. Toward this, an accurate knowledge of the road edges increases the reliability and precision of extracting other road features. We have developed an automated algorithm for extracting road edges from mobile laser scanning (MLS) data based on the parametric active contour or snake model. The algorithm involves several internal and external energy parameters that need to be analyzed in order to find their optimal values. In this paper, we present a detailed analysis of the snake energy parameters involved in our road edge extraction algorithm. Their optimal values enable us to automate the process of extracting edges from MLS data for tested road sections. We present a modified external energy in our algorithm and demonstrate its utility for extracting road edges from low and nonuniform point density datasets. A novel validation approach is presented, which provides a qualitative assessment of the extracted road edges based on direct comparisons with reference road edges. This approach provides an alternative to traditional road edge validation methodologies that are based on creating buffer zones around reference road edges and then computing quality measure values for the extracted edges. We tested our road edge extraction algorithm on datasets that were acquired using multiple MLS systems along various complex road sections. The successful extraction of road edges from these datasets validates the robustness of our algorithm for use in complex route corridor environments.Index Terms-Complex road, mobile laser scanning (MLS), road edges, snake energy, validation approach.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Snake Energy Analysis and Result Validation for a Mobile Laser Scanning Data-Based Automated Road Edge Extraction Algorithm

Kumar

Lewis

McElhinney

et al. 2017

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

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“…However, these algorithms are computationally intensive and time-consuming. Edge-based methods [32][33][34][35][36][37] first detect and fit linear road edges, and then segment the road surface using these edges. Point cloud features, such as the normal vector [32] and elevation difference [33], are commonly used to detect road edges or curbs.…”

Section: Introductionmentioning

confidence: 99%

“…Edge-based methods [32][33][34][35][36][37] first detect and fit linear road edges, and then segment the road surface using these edges. Point cloud features, such as the normal vector [32] and elevation difference [33], are commonly used to detect road edges or curbs. To improve the computational efficiency, scan lines [34], grids [35] and voxels [36] are used as the basis of road extraction from the point cloud.…”

Section: Introductionmentioning

confidence: 99%

Combined Lane Mapping Using a Mobile Mapping System

et al. 2019

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High-definition mapping of 3D lane lines has been widely needed for the highway documentation and intelligent navigation of autonomous systems. A mobile mapping system (MMS) captures both accurate 3D LiDAR point clouds and high-resolution images of lane markings at highway driving speeds, providing an abundant data source for combined lane mapping. This paper aims to map lanes with an MMS. The main contributions of this paper include the following: (1) an intensity correction method was introduced to eliminate the reflectivity inconsistency of road-surface LiDAR points; (2) a self-adaptive thresholding method was developed to extract lane markings from their complicated surroundings; and (3) a LiDAR-guided textural saliency analysis of MMS images was proposed to improve the robustness of lane mapping. The proposed method was tested with a dataset acquired in Wuhan, Hubei, China, which contained straight roads, curved roads, and a roundabout with various pavement markings and a complex roadside environment. The experimental results achieved a recall of 96.4%, a precision of 97.6%, and an F-score of 97.0%, demonstrating that the proposed method has strong mapping ability for various urban roads.

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“…The on-road points occupy the biggest part of MLS points in road. Extraction methods [2,[9][10][11][12][13][14] aimed at detecting and extracting on-road objects (e.g., driving lines, road boundaries, road cracks and road manholes) performed well both in accuracy and precision. The off-road points could be used to identify and extract traffic signs, trees, power lines and light poles, holes, and cracks in sidewalks.…”

mentioning

confidence: 99%

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

et al. 2018

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Railroads companies conduct regular inspections of their tracks to maintain and update the geographic data for railway management. Traditional railroad inspection methods, such as onsite inspections and semi-automated analysis of imagery and video data, are time consuming and ineffective. This study presents an automated effective method to detect tracks on the basis of their physical shape, geometrical properties, and reflection intensity feature. This study aims to investigate the feasibility of fast extraction of railroad using onboard Velodyne puck data collected by mobile laser scanning (MLS) system. Results show that the proposed method can be executed rapidly on an i5 computer with at least 10 Hz. The MLS system used in this study comprises a Velodyne puck/onboard GNSS receiver/inertial measurement unit. The range accuracy of Velodyne puck equipment is 2 cm, which fulfills the need of precise mapping. Notably, positioning STD is lower than 4 cm in most areas. Experiments are also undertaken to evaluate the timing of the proposed method. Experimental results indicate that the proposed method can extract 3D tracks in real-time and correctly recognize pairs of tracks. Accuracy, precision, and sensitivity of total test area are 99.68%, 97.55%, and 66.55%, respectively. Results suggest that in a multi-track area, close collaboration between MLS platforms mounted on several trains is required. terrestrial laser scanning (TLS), and MLS is common in recent years because laser scanners provide massive high-quality range measurements with intensity features in a fast manner. Many remarkable practices are made in MLS, ALS, and TLS. The ALS technology has been commercialized in the generation of high-precision digital elevation and surface models [3]. The first MMS [4] used onboard satellite receivers and stereo cameras to capture road images. At that time, precise point positioning [5] technology and differential positioning [6] technology were not introduced yet. IMUs are expensive and the application of mobile mapping systems (MMSs) is limited. Currently, Crucial high-end MMSs such as the Optech Lynx mobile mapping system, Trimble MX-9 MLS system, and RIGEL VMX-2HA MLS system could capture over millions of points every second. Commercial MMSs are applied to point cloud data acquisition of urban road networks and railroad corridors. However, commercial MMSs do not provide a user programming interface, which makes object detection and extraction applications based on them impossible. Consequently, previous studies were focused on the combined use of those devices [7].In general terms, an MLS platform is a vehicle-mounted MMS that is integrated with multiple sensors (e.g., GNSS antenna, IMU, digital cameras) and equipped with a centralized computing system for data synchronization and management. Existing urban reconstruction algorithms using data from different source (e.g., ALS, TLS, MLS) obtain convincing results in urban object modeling and management. Data collected by vehicle-mounted MMSs can be used to detect o...

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Road Boundaries Detection Based on Local Normal Saliency From Mobile Laser Scanning Data

Cited by 65 publications

References 10 publications

Snake Energy Analysis and Result Validation for a Mobile Laser Scanning Data-Based Automated Road Edge Extraction Algorithm

Snake Energy Analysis and Result Validation for a Mobile Laser Scanning Data-Based Automated Road Edge Extraction Algorithm

Combined Lane Mapping Using a Mobile Mapping System

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

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