Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Pastucha, Elżbieta

doi:10.3390/rs8100801

Cited by 33 publications

(29 citation statements)

References 34 publications

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“…Regarding to optical or laser scanning measurements the appropriate kind of the platform should be considered. In the project, the authors used a tripod for TLS, but it is possible to acquire data by using a platform on a moving vehicle which could be called as mobile 13,14,15 .…”

Section: Fig 6 the Results Of Tls And Als Alignmentmentioning

confidence: 99%

Implementation of Spatial Information for Monitoring and Analysis of the Area Around the Port Using Laser Scanning Techniques

Bobkowska

Inglot

Mikušová

et al. 2017

Polish Maritime Research

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Nowadays, maritime infrastructure is heavily exploited, which requires monitoring. The article presents the implementation of spatial information which are point clouds for monitoring and analysis of the area around the port (buildings and wharves). For this study, point clouds coming from terrestrial (TLS) and airborne laser scanning (ALS), each of them having different accuracy, were used. An important part of the analysis was the integration of the two data sources. Through integration, the acquisition of information on areas not covered by the measurement in the presented case, one of the methods was possible for use (e.g. the roofs in case of TLS, or the lack of some of the walls of buildings in case of ALS).Another aspect was to use this data. Measurement of the shape and geometry of objects was executed. Additionally, the planeness analysis of individual elements of port infrastructure has been carried out. An interesting analysis was to determine the water level, based on relation to specific characteristics of the light reflectance.

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Section: Fig 6 the Results Of Tls And Als Alignmentmentioning

confidence: 99%

Implementation of Spatial Information for Monitoring and Analysis of the Area Around the Port Using Laser Scanning Techniques

Bobkowska

Inglot

Mikušová

et al. 2017

Polish Maritime Research

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“…The data-driven and model-driven methods of automatic extraction of rails from the point cloud data of the MMS without other sources can be classified into two categories, one uses local features and the other uses global statistical characteristics [2,25,27]. Yang et al [24,33] used the high-difference fluctuations on the local scanning line considering the low-reflection characteristics of the steel structure.…”

Section: Related Workmentioning

confidence: 99%

“…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. Pastucha et al [27] used the MMS trajectory to limit the search area vertically and horizontally and extracted of catenaries. This method Remote Sens.…”

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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“…The operational condition of OCS can be acquired by analyzing the MLS point cloud instead of manual measurement. Recognizing the point cloud of OCS as a critical task of the data analysis has been studied in previous studies (e.g., [6][7][8]). Mobile 2D LiDAR is a special kind of MLS system applied to railway inspections.…”

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confidence: 99%

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

Chen

Lin

et al. 2020

Sensors

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The overhead contact system (OCS) is a critical railway infrastructure for train power supply. Periodic inspections, aiming at acquiring the operational condition of the OCS and detecting problems, are necessary to guarantee the safety of railway operations. One of the OCS inspection means is to analyze data of point clouds collected by mobile 2D LiDAR. Recognizing OCS components from the collected point clouds is a critical task of the data analysis. However, the complex composition of OCS makes the task difficult. To solve the problem of recognizing multiple OCS components, we propose a new deep learning-based method to conduct semantic segmentation on the point cloud collected by mobile 2D LiDAR. Both online data processing and batch data processing are supported because our method is designed to classify points into meaningful categories of objects scan line by scan line. Local features are important for the success of point cloud semantic segmentation. Thus, we design an iterative point partitioning algorithm and a module named as Spatial Fusion Network, which are two critical components of our method for multi-scale local feature extraction. We evaluate our method on point clouds where sixteen categories of common OCS components have been manually labeled. Experimental results show that our method is effective in multiple object recognition since mean Intersection-over-Unions (mIoUs) of online data processing and batch data processing are, respectively, 96.12% and 97.17%.well record geometric details of surrounding objects [5]. Therefore, it is appropriate to apply MLS systems to OCS inspections. The operational condition of OCS can be acquired by analyzing the MLS point cloud instead of manual measurement. Recognizing the point cloud of OCS as a critical task of the data analysis has been studied in previous studies (e.g., [6][7][8]). Mobile 2D LiDAR is a special kind of MLS system applied to railway inspections. Figure 1 shows an instance of the mobile 2D LiDAR used to scan the OCS infrastructure. As for this kind of MLS system, a point cloud is built up by integrating points at each 2D scan line. Fully understanding the point cloud is significant for automatic inspections and intelligent diagnoses. Thus, this study focuses on multiple OCS component recognition with mobile 2D LiDAR. However, it is a difficult task because the similarities and the various associations among OCS components make scenes complex [8]. In this case, model-driven and data-driven methods become incompetent because rules and features for recognizing specific objects are difficult to be designed by human beings. Fortunately, the success of deep learning-based image recognition (e.g., [9][10][11]) has promoted the fast development of deep learning-based point cloud segmentation, which provides novel means to understand point cloud with semantics.

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Catenary System Detection, Localization and Classification Using Mobile Scanning Data

Cited by 33 publications

References 34 publications

Implementation of Spatial Information for Monitoring and Analysis of the Area Around the Port Using Laser Scanning Techniques

Implementation of Spatial Information for Monitoring and Analysis of the Area Around the Port Using Laser Scanning Techniques

A Fast Algorithm for Rail Extraction Using Mobile Laser Scanning Data

A Deep Learning-Based Method for Overhead Contact System Component Recognition Using Mobile 2D LiDAR

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