An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds

Arastounia, Mostafa

doi:10.3390/infrastructures2020008

Cited by 21 publications

(30 citation statements)

References 12 publications

(37 reference statements)

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“…For example, filtering can be based on the outlier characteristics of points and geometric relationships with other points. Arastounia et al [26,29,30] proposed an approach based on the statistical method of the global map. The height difference of the track bed is small, and the standard deviation in the fixed neighborhood is calculated in accordance with the histogram statistical method, thereby obtaining the threshold for distinguishing the track bed from other targets.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, railroad infrastructures like tracks, wires, masses, towers, signs, bridges are of vital importance to railroad management and requires monitoring for safe transportation. Automated methods [25][26][27][28][29][30] to recognize railroad infrastructure such as overhead catenary systems from 3D LIDAR data, are later developed.In terms of the real-time processing of MLS data, Simultaneous Localization and Mapping technology has developed rapidly in recent years. Considerable research has been made to extract features from raw point cloud to realize efficient and accurate scan matching.…”

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confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

et al. 2018

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“…Geodatasets on railway infrastructure are useful resources in the transportation sector for routing applications and planning construction activities [1][2][3]. From the maintenance perspective, it is necessary to periodically examine the infrastructure for defects and damages that might affect the normal operation of trains [2,[4][5][6]. Traditionally, the infrastructure is mapped by terrestrial surveys using tachymeters and total stations.…”

Section: Introductionmentioning

confidence: 99%

“…Although the employed methods are highly accurate, they are time-consuming and expensive [1,4]. Therefore, research is driven towards algorithms that automatically map the railway infrastructure from data collected using a wide range of sensors such as digital cameras, laser scanners, global navigation satellite system (GNSS) receivers and eddy current sensors (ECS) [1][2][3][4][5][6][7][8]. By following methods based on sensor data, both the cost and time are significantly reduced.…”

Section: Introductionmentioning

confidence: 99%

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

et al. 2020

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Up-to-date geodatasets on railway infrastructure are valuable resources for the field of transportation. This paper investigates three methods for mapping the center lines of railway tracks using heterogeneous sensor data: (i) conditional selection of satellite navigation (GNSS) data, (ii) a combination of inertial measurements (IMU data) and GNSS data in a Kalman filtering and smoothing framework and (iii) extraction of center lines from laser scanner data. Several combinations of the methods are compared with a focus on mapping in tree-covered areas. The center lines of the railway tracks are extracted by applying these methods to a test dataset collected by a road-rail vehicle. The guard rails in the test area were also extracted during the center line detection process. The combination of methods (i) and (ii) gave the best result for the track on which the measurement vehicle had moved, mapping almost 100% of the track. The combination of methods (ii) and (iii) and the combination of all three methods gave the best result for the other parallel tracks, mapping between 25% and 80%. The mean perpendicular distance of the mapped center lines from the reference data was 1.49 meters.

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“…In [19], the method uses an integrated data-driven and model-driven approach, enhanced by fitting a 2D grid to track bed and by employing template matching to eliminate rail track false positives. The algorithm presented in [20] makes the classification of a small part of the cloud points (based on height), allowing the application for a railway with small slopes, which then uses the Eigen decomposition to select the cloud points representing the rail tracks, ensuring the method operationality regardless their size. The work presented in [21] proposes a Conditional Random Field classifier developed based, which would be the MLS point clouds converted into a set of line segments to which the labelling process is applied.…”

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confidence: 99%

Automated Three-Dimensional Linear Elements Extraction from Mobile LiDAR Point Clouds in Railway Environments

Gézero

Antunes

2019

Infrastructures

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The railway structures need constant monitoring and maintenance to ensure the train circulation safety. Quality information concerning the infrastructure geometry, namely the three-dimensional linear elements, are crucial for that processes. Along with this work, a method to automated extract three-dimensional linear elements from point clouds collected by terrestrial mobile LiDAR systems along railways is presented. The proposed method takes advantage of the stored cloud point's attributes as an alternative to complex geometric methods applied over the point's cloud coordinates. Based on the assumption that the linear elements to extract are roughly parallel to the rail tracks and therefore to the system trajectory, the stored scan angle value was used to restrict the number of cloud points that represents the linear elements. A simple algorithm is then applied to that restricted number of points to get the three-dimensional polylines geometry. The obtained values of completeness, correctness and quality, validate the use of the methodology for linear elements extraction from mobile LiDAR data gathered along railway environments.Infrastructures 2019, 4, 46 2 of 15 the need for pedestrian access to the railway, while gathering dense and precise information of the railway surrounding area. There are essentially two MLS installation solutions to collect the point clouds along the railway: mounted directly on the train or mounted on a car transported by the train. Despite the different advantages and disadvantages of those different solutions, there are no significant accuracy differences in the obtained cloud points [1].However, the MLS gathered point clouds are non-selective, i.e., they represent all of the elements in the sensor surrounding at a given moment, without any sort of classification. This makes it impossible to distinguish between points that represent the ground, vegetation, poles, rail tracks, persons or any other object within the sensor range. It is then the necessary method's implementation that allows an efficient classification of the points that represents a specific object among the millions of the cloud points.In last few years, the use of LiDAR data has progressively increased, and several studies have been published where this type of data are used for railway infrastructures monitoring, such as tunnels [2,3], railway gauge [1], railway bridges [4], rail surface monitoring and quality indexing [5] and rail surface defect detection [6]. Some studies have focused on extracting point elements from MLS data, like catenary systems [7], poles [8], trees [9] and traffic signals [10]. In [11], previous existing algorithms are adapted to extract several nonlinear objects from the railway environments, namely buildings [12], electric poles and vertical signals [13], trees [14] and ground points [15].Regarding linear elements extraction, the automatic extraction of railway tracks lines is clearly the most recurrent subject in the existing bibliography about the use of MLS point clouds in railway envir...

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An Enhanced Algorithm for Concurrent Recognition of Rail Tracks and Power Cables from Terrestrial and Airborne LiDAR Point Clouds

Cited by 21 publications

References 12 publications

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

Automatic Mapping of Center Line of Railway Tracks using Global Navigation Satellite System, Inertial Measurement Unit and Laser Scanner

Automated Three-Dimensional Linear Elements Extraction from Mobile LiDAR Point Clouds in Railway Environments

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