scholarly journals Self-Organized Model Fitting Method for Railway Structures Monitoring Using LiDAR Point Cloud

2020 ◽  
Vol 12 (22) ◽  
pp. 3702
Author(s):  
Amila Karunathilake ◽  
Ryohei Honma ◽  
Yasuhito Niina
Keyword(s):  
Point Cloud ◽  
Spatial Location ◽  
Railway Track ◽  
Computational Time ◽  
Accurate Estimation ◽  
Long Distance ◽  
Rail Track ◽  
Railway Line ◽  
Point Density ◽  
Vertical Slice

Mobile laser scanning (MLS) has been successfully used for infrastructure monitoring apt to its fine accuracy and higher point density, which is favorable for object reconstruction. The massive data size, computational time, wider spatial distribution and feature extraction become a challenging task for 3D point data processing with MLS point cloud receives from terrestrial structures such as buildings, roads and railway tracks. In this paper, we propose a new approach to detect the structures in-line with railway track geometry such as railway crossings, turnouts and quantitatively estimate their dimensions and spatial location by iteratively applying a vertical slice to point cloud data for long distance laser measurement. The rectangular vertical slices were defined and their boundary coordinates were estimated based on a geometrical method. Estimated vertical slice boundaries were iteratively used to evaluate the point density of each vertical slice along with a cross-track direction of the railway line. Those point densities were further analyzed to detect the railway line track objects by their shape and spatial location along with the rail bed. Herein, the survey dataset is used as a dictionary to preidentify the spatial location of the object and then as an accurate estimation for the rail-track, by estimating the gauge corner (GC) from dense point cloud. The proposed method has shown a significant improvement in the rail-track extraction process, which becomes a challenge for existing remote sensing technologies. This adaptive object detection method can be used to identify the railway track structures prior to the railway track extraction, which allows in finding the GC position precisely. Further, it is based on the parallelism of the railway track, which is distinct from conventional railway track extraction methods. Therefore it does not require any inertial measurements along with the MLS survey and can be applied with less background information of the observed MLS point cloud. The proposed algorithm was tested for the MLS data set acquired during the pilot project collaborated with West Japan Railway Company. The results indicate 100% accuracy for railway structure detection and enhance the GC extraction for railway structure monitoring.

scholarly journals Extracting Rail Track Geometry from Static Terrestrial Laser Scans for Monitoring Purposes

2014 ◽  
Vol XL-5 ◽  
pp. 553-557 ◽  
Author(s):  
A. Soni ◽  
S Robson ◽  
B Gleeson
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Railway Track ◽  
Monitoring Tool ◽  
Track Geometry ◽  
Rail Track ◽  
Target Locations ◽  
Track Monitoring

This paper presents the capabilities of detecting relevant geometry of railway track for monitoring purposes from static terrestrial laser scanning (TLS) systems at platform level. The quality of the scans from a phased based scanner (Scanner A) and a hybrid timeof- flight scanner (Scanner B) are compared by fitting different sections of the track profile to its matching standardised rail model. The various sections of track investigated are able to fit to the model with an RMS of less than 3 mm. Both scanners show that once obvious noise and artefacts have been removed from the data, the most confident fit of the point cloud to the model is the section closest to the scanner position. The results of the fit highlight the potential to use this method as a bespoke track monitoring tool during major redevelopment projects where traditional methods, such as robotic total stations, results in missed information, for example due to passing trains or knocked prisms and must account for offset target locations to compute track parameters.

scholarly journals A PARALLEL ALGORITHM FOR LOCAL POINT DENSITY INDEX COMPUTATION OF LARGE POINT CLOUDS

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 75-82
Author(s):  
A. V. Vo ◽  
C. N. Lokugam Hewage ◽  
N. A. Le Khac ◽  
M. Bertolotto ◽  
D. Laefer
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Computational Time ◽  
Data Set ◽  
Input Point ◽  
Processing Step ◽  
Local Point ◽  
Density Index ◽  
Point Density ◽  
Point Data

Abstract. Point density is an important property that dictates the usability of a point cloud data set. This paper introduces an efficient, scalable, parallel algorithm for computing the local point density index, a sophisticated point cloud density metric. Computing the local point density index is non-trivial, because this computation involves a neighbour search that is required for each, individual point in the potentially large, input point cloud. Most existing algorithms and software are incapable of computing point density at scale. Therefore, the algorithm introduced in this paper aims to address both the needed computational efficiency and scalability for considering this factor in large, modern point clouds such as those collected in national or regional scans. The proposed algorithm is composed of two stages. In stage 1, a point-level, parallel processing step is performed to partition an unstructured input point cloud into partially overlapping, buffered tiles. A buffer is provided around each tile so that the data partitioning does not introduce spatial discontinuity into the final results. In stage 2, the buffered tiles are distributed to different processors for computing the local point density index in parallel. That tile-level parallel processing step is performed using a conventional algorithm with an R-tree data structure. While straight-forward, the proposed algorithm is efficient and particularly suitable for processing large point clouds. Experiments conducted using a 1.4 billion point data set acquired over part of Dublin, Ireland demonstrated an efficiency factor of up to 14.8/16. More specifically, the computational time was reduced by 14.8 times when the number of processes (i.e. executors) increased by 16 times. Computing the local point density index for the 1.4 billion point data set took just over 5 minutes with 16 executors and 8 cores per executor. The reduction in computational time was nearly 70 times compared to the 6 hours required without parallelism.

The prediction of vehicle vibration transmitted to the occupant using a modular transfer matrix

2021 ◽  
pp. 107754632199759
Author(s):  
Jianchun Yao ◽  
Mohammad Fard ◽  
John L Davy ◽  
Kazuhito Kato
Keyword(s):  
Finite Element ◽  
Transfer Matrix ◽  
Dynamic Performance ◽  
Complex Structure ◽  
Vehicle Body ◽  
Computational Time ◽  
Accurate Estimation ◽  
Finite Element Models ◽  
Transfer Matrices ◽  
Body Vibration

Industry is moving towards more data-oriented design and analyses to solve complex analytical problems. Solving complex and large finite element models is still challenging and requires high computational time and resources. Here, a modular method is presented to predict the transmission of vehicle body vibration to the occupants’ body by combining the numerical transfer matrices of the subsystems. The transfer matrices of the subsystems are presented in the form of data which is sourced from either physical tests or finite element models. The structural dynamics of the vehicle body is represented using a transfer matrix at each of the seat mounting points in three triaxial (X–Y–Z) orientations. The proposed method provides an accurate estimation of the transmission of the vehicle body vibration to the seat frame and the seated occupant. This method allows the combination of conventional finite element analytical model data and the experimental data of subsystems to accurately predict the dynamic performance of the complex structure. The numerical transfer matrices can also be the subject of machine learning for various applications such as for the prediction of the vibration discomfort of the occupant with different seat and foam designs and with different physical characteristics of the occupant body.

scholarly journals Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

2021 ◽  
Vol 11 (5) ◽  
pp. 2268
Author(s):  
Erika Straková ◽  
Dalibor Lukáš ◽  
Zdenko Bobovský ◽  
Tomáš Kot ◽  
Milan Mihola ◽  
...  
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Language Models ◽  
Computational Time ◽  
Dominant Eigenvalue ◽  
Eigenvalues And Eigenvectors ◽  
Correct Model ◽  
Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

scholarly journals Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

2021 ◽  
Vol 13 (13) ◽  
pp. 2494
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Point Cloud ◽  
Goodness Of Fit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Statistical Testing ◽  
Computational Time ◽  
Terrestrial Laser Scanner ◽  
Reference Surface ◽  
Local Refinement ◽  
Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

J and CTOD Based Tensile Strain Capacity Prediction for Pipelines with a Surface Crack in Girth Weld

2021 ◽  
Author(s):  
Youn-Young Jang ◽  
Nam-Su Huh ◽  
Ik-Joong Kim ◽  
Young-Pyo Kim
Keyword(s):  
Crack Initiation ◽  
Internal Pressure ◽  
Surface Crack ◽  
Driving Force ◽  
Tensile Strain ◽  
Structural Integrity ◽  
Accurate Estimation ◽  
Long Distance ◽  
Crack Driving Force ◽  
Girth Welding

Abstract Long-distance pipelines for the transport of oil and natural gas to onshore facilities are mainly fabricated by girth welding, which has been considered as a weak location for cracking. Pipeline rupture due to crack initiation and propagation in girth welding is one of the main issues of structural integrity for a stable supply of energy resources. The crack assessment should be performed by comparing the crack driving force with fracture toughness to determine the critical point of fracture. For this reason, accurate estimation of the crack driving force for pipelines with a crack in girth weld is highly required. This paper gives the newly developed J-integral and crack-tip opening displacement (CTOD) estimation in a strain-based scheme for pipelines with an internal surface crack in girth weld under axial displacement and internal pressure. For this purpose, parametric finite element analyses have been systematically carried out for a set of pipe thicknesses, crack sizes, strain hardening, overmatch and internal pressure conditions. Using the proposed solutions, tensile strain capacities (TSCs) were quantified by performing crack assessment based on crack initiation and ductile instability and compared with TSCs from curved wide plate tests to confirm their validity.

scholarly journals Investigation of horizontal forces result in railway viaduct with curved truck

2013 ◽  
Vol 12 (2) ◽  
pp. 047-054 ◽  
Author(s):  
Piotr Olaszek ◽  
Juliusz Cieśla ◽  
Waldemar Szaniec
Keyword(s):  
Railway Track ◽  
Bridge Structure ◽  
Dynamic Effects ◽  
Passenger Cars ◽  
Limit States ◽  
Railway Line ◽  
Vertical Displacements ◽  
Special Train ◽  
Ultimate Limit ◽  
Crossing Test

In the report some investigations of bridge structure, connected with the adaptation of the railway line to speeds up to 200 km/h for conventional trains and up to 250 km/h for tilting trains were presented. A railway track is the characteristic feature of tested viaduct, because the truck is curved over the whole length of span with radius of R = 2600 m. The tests of the viaduct required the verification of influence of the dynamic effects on the ultimate limit states which corresponded to the safety of structure, as well as the serviceability limit states, related to the safety of driving and the travellers’ comfort. In frames of investigations, a special train comprised of two locomotives and four passenger cars, was used with speeds in the range between 10 and 200 km/h. The report focuses on the problems addressing the influence of horizontal actions in the case of bridge with curved truck. The measurements of the horizontal and vertical displacements as well as the accelerations of span, and the speed of crossing test train were executed. The measured and theoretically calculated chosen courses of displacements and accelerations were introduced. The degree of divergence between measured and calculated values was analysed.

scholarly journals A ROBUST ESTIMATION TECHNIQUE FOR 3D POINT CLOUD REGISTRATION

2016 ◽  
Vol 35 (1) ◽  
pp. 15 ◽  
Author(s):  
Dhanya S Pankaj ◽  
Rama Rao Nidamanuri
Keyword(s):  
Robust Estimation ◽  
Point Cloud ◽  
Feature Matching ◽  
Computational Time ◽  
Model Estimation ◽  
3D Registration ◽  
3D Images ◽  
Data Points ◽  
Pairwise Registration ◽  
3D Scans

The 3D modeling pipeline involves registration of partially overlapping 3D scans of an object. The automatic pairwise coarse alignment of partially overlapping 3D images is generally performed using 3D feature matching. The transformation estimation from matched features generally requires robust estimation due to the presence of outliers. RANSAC is a method of choice in problems where model estimation is to be done from data samples containing outliers. The number of RANSAC iterations depends on the number of data points and inliers to the model. Convergence of RANSAC can be very slow in the case of large number of outliers. This paper presents a novel algorithm for the 3D registration task which provides more accurate results in lesser computational time compared to RANSAC. The proposed algorithm is also compared against the existing modifications of RANSAC for 3D pairwise registration. The results indicate that the proposed algorithm tends to obtain the best 3D transformation matrix in lesser time compared to the other algorithms.

Three-Dimensional Point Cloud Classification of Large Outdoor Scenes Based on Vertical Slice Sampling and Centroid Distance Histogram

2018 ◽  
Vol 45 (10) ◽  
pp. 1004001
Author(s):  
佟国峰 Tong Guofeng ◽  
杜宪策 Du Xiance ◽  
李勇 Li Yong ◽  
陈槐嵘 Chen Huairong ◽  
张庆春 Zhang Qingchun
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Slice Sampling ◽  
Cloud Classification ◽  
Centroid Distance ◽  
Outdoor Scenes ◽  
Vertical Slice ◽  
Point Cloud Classification ◽  
Distance Histogram
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