Analysis of the Measuring Model of Auto-Evaluation System of Railway Coupler

2014 ◽  
Vol 644-650 ◽  
pp. 1118-1121
Author(s):  
Jin Huang ◽  
Si Han Chen ◽  
Kai Zheng ◽  
Feng Hua Zhang
Keyword(s):  
Evaluation System ◽  
Point Cloud ◽  
Detection Method ◽  
Complex Structure ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Movement Mechanism

Due to the complex structure of railway coupler, the evaluation of coupler is always a difficult task for railway carriage inspection at present. Combining structural light scanning technology with movement mechanism, a coupler auto-evaluation system has been designed and realized. This paper emphasizes on analyzing the measuring model of the coupler evaluation system, and introducing the point cloud data processes for getting evaluation items. The system is more flexible and efficient than traditional detection method of coupler, and can meet the requirements of auto-evaluation items of coupler.

scholarly journals An Innovative Detection Method of High-Speed Railway Track Slab Supporting Block Plane Based on Point Cloud Data from 3D Scanning Technology

2019 ◽  
Vol 9 (16) ◽  
pp. 3345 ◽  
Author(s):  
Chen ◽  
Qin ◽  
Xia ◽  
Bao ◽  
Huang ◽  
...  
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
Detection Method ◽  
Railway Track ◽  
Point Cloud Data ◽  
3D Scanner ◽  
High Speed Railway ◽  
Cloud Data ◽  
Plane Point ◽  
Track Slab

The dimension detection of high-speed railway track slabs is one of the most important tasks before the track slabs delivery. Based on the characteristics of a 3D scanner which can acquire a large amount of measurement data continuously and rapidly in a short time, this paper uses the integration of 3D scanner and the intelligent robot to detect the CRTSIII (China Railway Track System) track slab supporting block plane, then the dense and accurate supporting block plane point cloud data is obtained, and the point cloud data is registered with the established model. An improved Random Sample Consensus (RANSAC) plane fitting algorithm is also proposed to extract the data of supporting block plane point cloud in this paper. The detection method is verified and the quality analysis of the detection results is assessed by a lot of real point cloud data obtained on site. The results show that the method can meet the quality control of CRTSIII finished track slab and the detection standard. Compared with the traditional detection methods, the detection method proposed in this paper can complete the detection of a track slab in 7 min, which greatly improves the detection efficiency, and has better reliability. The method has wide application prospects in the field of railway component detection.

scholarly journals Building 3D Realistic Modeling Based on Air-ground Multi-source Data Fusion

2020 ◽  
Vol 213 ◽  
pp. 03025
Author(s):  
Yan Wang ◽  
Tingting Zhang ◽  
Jingyi Wang
Keyword(s):  
Data Fusion ◽  
Point Cloud ◽  
Laser Scanning ◽  
Spatial Information ◽  
Complex Structure ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Source Data

Three-dimensional point cloud data is a new form of three-dimensional collection, which not only contains the geometric topology information of the object, but also has high simplicity and flexibility. In this paper, the air-ground multi-source data fusion technology is used to study the fine reconstruction of the 3D scene: based on the 3D laser scanning laser point cloud, the 3D spatial information of the ground visible objects is obtained, and the orthophoto obtained by the drone aerial photography is assisted, Obtain the three-dimensional space information of the top of the ground feature, and the ground three-dimensional laser scanner can quickly obtain the three-dimensional surface information of the building facade, ground, and trees. Due to the complex structure of the building and the occlusion of spatial objects, sub-station scanning is required when acquiring point cloud data. This article uses the Sino-German Energy Conservation Center Building of Shenyang Jianzhu University as the research area, using drone tilt photography technology and ground lidar technology to integrate. During the experiment, the field industry adopted the UAV image acquisition strategy of “automatic shooting of regular routes, supplemented by manual shooting of areas of interest”; in the field industry, the method of “manual coarse registration and ICP algorithm fine registration” The example results show that the ground 3D laser point cloud air-ground image fusion 3D modeling effect proposed in this paper is better and the quality is greatly improved, which makes up for the ground 3D laser scanning. In point cloud modeling, a large number of holes are insufficient due to occlusion and missing top information.

Building Damage Detection Method Based on Normal Vector Computation of Airborne Point Cloud Data

2021 ◽  
Author(s):  
Shusong Huang ◽  
Yong Zeng ◽  
Wei Yi ◽  
Weirong Chen ◽  
Wenbo Su ◽  
...  
Keyword(s):  
Damage Detection ◽  
Point Cloud ◽  
Detection Method ◽  
Building Damage ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Cloud Data

A Person Detection Method Using 3D Laser Scanner – Proposal of Efficient Grouping Method of Point Cloud Data –

2015 ◽  
Vol 27 (4) ◽  
pp. 374-381 ◽  
Author(s):  
Kento Hosaka ◽  
◽  
Tetsuo Tomizawa
Keyword(s):  
Point Cloud ◽  
Detection Method ◽  
Laser Scanner ◽  
Experimental Results ◽  
Point Cloud Data ◽  
Shape Features ◽  
Person Detection ◽  
Cloud Data ◽  
Grouping Method ◽  
Individual Objects

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270004/07.jpg"" width=""300"" /> Our proposed method</div> The purpose of this study is to develop a system for detecting target persons using a 3D laser scanner. The system consists of two parts -- one for grouping and one for determining targets. The grouping part effectively segments individual objects by using two-step grouping. The target part determines target persons for grouping results using shape features. Experimental results showed that our proposed system detects targets as well as existing methods do and that our proposed method performs more quickly than existing methods do. </span>

scholarly journals Research on the Automatic Detection Method of Tunnel Clearance Based on Point Cloud Data

2020 ◽  
Author(s):  
Jun Han ◽  
Guodong Chen ◽  
Tao Liu ◽  
Qian Yang
Keyword(s):  
Point Cloud ◽  
Field Experiments ◽  
Detection Method ◽  
Automatic Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Tunnel Detection ◽  
Low Efficiency

Due to the deformation of the tunnel and the abnormal outburst of internal facilities, the existing railway tunnel line needs to be inspected regularly. However, the existing detection methods have some shortcomings, such as large measurement interference, low efficiency, discontinuity of section, and independence with the track structure. Therefore, an automatic detection method of tunnel space clearance based on point cloud data is proposed. By fitting the central axis of the tunnel, the extraction can be realized at any position of the tunnel. The coordinate system of tunnel gauge detection based on rail top surface is established, and different types of tunnel gauge frames are introduced. The improved ray algorithm method is used to realize automatic detection and analysis of various tunnel types. Field experiments on existing railway tunnels show that the method can accurately obtain the limit point and size of the tunnel. The cross-section of transgression is obtained. It can meet the requirements of tunnel detection accuracy and has great practicability in tunnel disease detection.

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