scholarly journals Analyzing the Stability of Underground Mines Using 3D Point Cloud Data and Discontinuum Numerical Analysis

2019 ◽  
Vol 11 (4) ◽  
pp. 945 ◽  
Author(s):  
Seung-Joong Lee ◽  
Sung-Oong Choi
Keyword(s):  
Numerical Analysis ◽  
Point Cloud ◽  
Target Surface ◽  
Underground Mines ◽  
Point Cloud Data ◽  
Cross Sectional ◽  
Cloud Data ◽  
Safety Pillar ◽  
Center Axis ◽  
The Stability

This study describes a precise numerical analysis process by adopting the real image of mine openings obtained by light detection and ranging (LiDAR), which can produce a point cloud data by measuring the target surface numerically. The analysis target was a section of an underground limestone mine, to which a hybrid room-and-pillar mining method that was developed to improve ore recovery was applied. It is important that the center axis and the volume of the vertical safety pillar in the lower parts match those in the upper parts. The 3D survey of the target section verified that the center axis of the vertical safety pillar in the lower parts had deviated in a north-westerly direction. In particular, the area of the lower part of the vertical safety pillar was approximately 34 m2 lower than the designed cross-sectional area, which was 100 m2. In order to analyze the stability of the vertical safety pillar, a discontinuum numerical analysis and safety factor analysis were conducted using 3D surveying results. The analysis verified that instability was caused by the joints distributed around the vertical safety pillar. In conclusion, investigation of the 3D survey and 3D numerical analysis techniques performed in this study are expected to provide higher reliability than the current techniques used for establishing whether mining plans require new mining methods or safety measures.

Surface Design of Molding Tool for Composite Component Based on Reverse Engineering

2012 ◽  
Vol 503-504 ◽  
pp. 215-218 ◽  
Author(s):  
Da Wei Wu ◽  
Xiao Fei Ding ◽  
Gang Tong
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Laser Scanner ◽  
Surface Fitting ◽  
Point Cloud Data ◽  
Composite Component ◽  
Cross Sectional ◽  
Surface Design ◽  
Cloud Data ◽  
Molding Tool

This paper analyzes the structure of molding tool for composite component, and proposes a method of surface design of molding tool based on reverse engineering. By using handy laser scanner, the point cloud data is obtained from the composite component, which is processed in Geomagic Studio. Then the processed data is imported into CATIA for Surface fitting. The surface of molding tool for composite component is rapidly and accurately designed by analyzing 3D error and comparing cross-sectional data.

scholarly journals Efficient Calculation Method for Tree Stem Traits from Large-Scale Point Clouds of Forest Stands

2021 ◽  
Vol 13 (13) ◽  
pp. 2476
Author(s):  
Hiroshi Masuda ◽  
Yuichiro Hiraoka ◽  
Kazuto Saito ◽  
Shinsuke Eto ◽  
Michinari Matsushita ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Random Access ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Forest Stands ◽  
Cross Sectional ◽  
Cloud Data ◽  
Wireframe Model

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

scholarly journals ENVIRONMENTAL INFLUENCES ON THE STABILITY OF A PERMANENTLY INSTALLED LASER SCANNER

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 745-752
Author(s):  
M. Kuschnerus ◽  
D. Schröder ◽  
R. Lindenbergh
Keyword(s):  
Systematic Error ◽  
Point Cloud ◽  
Laser Scanner ◽  
Weather Station ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Mean ◽  
The Stability ◽  
The Impact ◽  
Mean Square Errors

Abstract. The advancement of permanently measuring laser scanners has opened up a wide range of new applications, but also led to the need for more advanced approaches on error quantification and correction. Time-dependent and systematic error influences may only become visible in data of quasi-permanent measurements. During a scan experiment in February/March 2020 point clouds were acquired every thirty minutes with a Riegl VZ-2000 laser scanner, and various other sensors (inclination sensors, weather station and GNSS sensors) were used to survey the environment of the laser scanner and the study site. Using this measurement configuration, our aim is to identify apparent displacements in multi-temporal scans due to systematic error influences and to investigate data quality for assessment of geomorphic changes in coastal regions. We analyse scan data collected around two storm events around 09/02/2020 (Ciara) and around 22/02/2020 (Yulia) and derive the impact of heavy storms on the point cloud data through comparison with the collected auxiliary data. To investigate the systematic residuals on data acquired by permanent laser scanning, we extracted several stable flat surfaces from the point cloud data. From a plane fitted through the respective surfaces of each scan, we estimated the mean displacement of each plane with the respective root mean square errors. Inclination sensors, internal and external, recorded pitch and roll values during each scan. We derived a mean inclination per scan (in pitch and roll) and the standard deviation from the mean as a measure of the stability of the laser scanner during each scan. Evaluation of the data recorded by a weather station together with knowledge of the movement behaviour, allows to derive possible causes of displacements and/or noise and correction models. The results are compared to independent measurements from GNSS sensors for validation. For wind speeds of 10 m/s and higher, movements of the scanner considerably increase the noise level in the point cloud data.

scholarly journals Quality Control Methodologies for 3D Geometric Primitives Derived from Lidar Data

2021 ◽  
Author(s):  
Arthur Fidera
Keyword(s):  
Numerical Analysis ◽  
Point Cloud ◽  
Goodness Of Fit ◽  
Three Dimensional ◽  
Critical Parameters ◽  
Condition Numbers ◽  
Computer Assisted ◽  
Point Cloud Data ◽  
Stability Criteria ◽  
Cloud Data

The purpose of this study is to develop algorithms with a computational ability to reliably establish with precision and accuracy the critical parameters of a solid object in space. Utilizing a least-squares adjustment method and laser scanned data, a three-dimensional computer assisted drawing (3D CAD) model of an object (e.g., machinery component) may be then used in the redesign, retrofitting, and updating of technical drawings. This thesis presents a unique approach to point cloud data modeling and visualization as well as numerical analysis based on stability criteria. Several statistical techniques from the literature are reviewed and implemented dealing with numerical methods using the stability of matrices as a criterion. The thesis discusses topics ranging from basic statistical analysis to advanced topics such as Singular Value Decomposition (SVD) and condition numbers. Various theories and techniques of obtaining stability criteria are described and analyzed. Test of point cloud data revealed that combining standard numerical analysis with Condition Numbers allows for quantifying the goodness-of-fit of the results and for predicting the behavior of the algorithms.

An efficient simplification method for point cloud based on salient regions detection

2019 ◽  
Vol 53 (2) ◽  
pp. 487-504 ◽  
Author(s):  
Abdul Rahman El Sayed ◽  
Abdallah El Chakik ◽  
Hassan Alabboud ◽  
Adnan Yassine
Keyword(s):  
Point Cloud ◽  
Processing Time ◽  
Point Clouds ◽  
The Other ◽  
Point Cloud Data ◽  
Weighted Graphs ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Salient Regions ◽  
The Stability

Many computer vision approaches for point clouds processing consider 3D simplification as an important preprocessing phase. On the other hand, the big amount of point cloud data that describe a 3D object require excessively a large storage and long processing time. In this paper, we present an efficient simplification method for 3D point clouds using weighted graphs representation that optimizes the point clouds and maintain the characteristics of the initial data. This method detects the features regions that describe the geometry of the surface. These features regions are detected using the saliency degree of vertices. Then, we define features points in each feature region and remove redundant vertices. Finally, we will show the robustness of our methodviadifferent experimental results. Moreover, we will study the stability of our method according to noise.

scholarly journals CMPC: An Innovative Lidar-Based Method to Estimate Tree Canopy Meshing-Profile Volumes for Orchard Target-Oriented Spray

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4252
Author(s):  
Chenchen Gu ◽  
Changyuan Zhai ◽  
Xiu Wang ◽  
Songlin Wang
Keyword(s):  
Point Cloud ◽  
Tree Canopy ◽  
Point Cloud Data ◽  
Cross Sectional ◽  
Cloud Data ◽  
Spray System ◽  
Canopy Volume ◽  
High Degree ◽  
Degree Of Similarity ◽  
Moving Speed

Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those obtained from manual measurements, and the measured canopy volume achieved an accuracy of 93.3%. Second, the feasibility of the method was verified by a field experiment where the canopy 3D stereogram and cross-sectional profiles were obtained via CMPC. The results show that the 3D stereogram exhibited a high degree of similarity with the tree canopy, although there were some differences at the edges, where the canopy was sparse. The CMPC-derived cross-sectional profiles matched the manually measured results well. The CMPC method achieved an accuracy of 96.3% when the tree canopy was detected by LiDAR at a moving speed of 1.2 m/s. The accuracy of the LiDAR system was virtually unchanged when the moving speeds was reduced to 1 m/s. No detection lag was observed when comparing the start and end positions of the cross-section. Different CMPC grid sizes were also evaluated. Small grid sizes (0.01 m × 0.01 m and 0.025 m × 0.025 m) were suitable for characterizing the finer details of a canopy, whereas grid sizes of 0.1 m × 0.1 m or larger can be used for characterizing its overall profile and volume. The results of this study can be used as a technical reference for the development of a LiDAR-based target-oriented spray system.

scholarly journals Quality Control Methodologies for 3D Geometric Primitives Derived from Lidar Data

2021 ◽  
Author(s):  
Arthur Fidera
Keyword(s):  
Numerical Analysis ◽  
Point Cloud ◽  
Goodness Of Fit ◽  
Three Dimensional ◽  
Critical Parameters ◽  
Condition Numbers ◽  
Computer Assisted ◽  
Point Cloud Data ◽  
Stability Criteria ◽  
Cloud Data

The purpose of this study is to develop algorithms with a computational ability to reliably establish with precision and accuracy the critical parameters of a solid object in space. Utilizing a least-squares adjustment method and laser scanned data, a three-dimensional computer assisted drawing (3D CAD) model of an object (e.g., machinery component) may be then used in the redesign, retrofitting, and updating of technical drawings. This thesis presents a unique approach to point cloud data modeling and visualization as well as numerical analysis based on stability criteria. Several statistical techniques from the literature are reviewed and implemented dealing with numerical methods using the stability of matrices as a criterion. The thesis discusses topics ranging from basic statistical analysis to advanced topics such as Singular Value Decomposition (SVD) and condition numbers. Various theories and techniques of obtaining stability criteria are described and analyzed. Test of point cloud data revealed that combining standard numerical analysis with Condition Numbers allows for quantifying the goodness-of-fit of the results and for predicting the behavior of the algorithms.

Study of Laser 3D Scanning Model Reconstruction for Nuclear Facilities Decommissioning

2020 ◽  
Author(s):  
Zhang Yongling ◽  
Zhao Wan ◽  
Wang Shuai
Keyword(s):  
Point Cloud ◽  
Radiation Safety ◽  
Laser Scanner ◽  
3D Scanning ◽  
Point Cloud Data ◽  
Model Reconstruction ◽  
Nuclear Facilities ◽  
Scanning Method ◽  
Cloud Data ◽  
The Stability

Abstract The laser 3D scanning model reconstruction is an effective technical means for the reconstruction of 3D virtual scenes of old nuclear facilities. However, the existing technology have the following problems: First, the scanning equipment needs to be installed on the triangular fixed bracket, which requires personnel to be installed on site, thereby bringing radiation safety risks to personnel; second, it requires the person to reconstruct and splice the model one face by one face, which is inefficient. Aiming to deal with these problems, this paper proposes a mobile laser 3D scanning method and a rapid model reconstruction technology. These methods include: (1) In order to realize fast scanning of large-scale scenes, and at the same time ensure accurate scanning of local important objects, a laser three-dimensional composite scanning method is proposed. (2) Design and development of a remote control automatic walking and fixing device, which can transport the laser 3D scanner to the position of measurement which is needed. The device has a certain ability to overcome obstacles, and can be reliably fixed to ensure the stability and scanning accuracy of the laser scanner at high speed working station. You can remotely control the laser scanner to start or stop working. The difficulty of this technology is how to make sure the device has the flexibility in motion and the stability on working; (3) An automatic model reconstruction and feature identification method based on a large number of point cloud data is proposed, and the corresponding software is developed. This method can realize the automatic recognition of the standard and common models, and the automatic model reconstruction of the point cloud data in the scene. To achieve this goal, the neural network algorithm is used. These technologies can effectively reduce the radiation safety risk of personnel during laser scanning in high-radioactive places, reduce the intensity of personnel operations, and improve work efficiency.

Error Inspection for Turbine Vane Based on Reverse Engineering

2011 ◽  
Vol 338 ◽  
pp. 335-338 ◽  
Author(s):  
Gang Tong ◽  
Yu Zhu Li ◽  
Da Wei Wu ◽  
Xiao Guang Han
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cross Sectional ◽  
Inspection Method ◽  
Cloud Data ◽  
Turbine Vane

An error inspection method based on 3D laser scanning measurement is proposed for the purpose of achieving field rapid inspection of turbine vane surface. The 3D model of vane is reconstructed by using the data of form drawing in CATIA. By using handy laser scanner, the point cloud data is obtained from the wood pattern of vane, which is processed in Geomagic Qualify. After registration of vane solid model and point cloud data, the vane surface is rapidly inspected by analyzing 3D error and comparing cross-sectional data.

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