scholarly journals A Multi-View Stereo Measurement System Based on a Laser Scanner for Fine Workpieces

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 381 ◽  
Author(s):  
Limei Song ◽  
Siyuan Sun ◽  
Yangang Yang ◽  
Xinjun Zhu ◽  
Qinghua Guo ◽  
...  
Keyword(s):  
Measurement System ◽  
Measurement Errors ◽  
Point Cloud ◽  
Laser Sensor ◽  
Point Cloud Data ◽  
Linear Stage ◽  
View Point ◽  
Cloud Data ◽  
Vision Measurement ◽  
Rotary Stage

A new solution to the high-quality 3D reverse modeling problem of complex surfaces for fine workpieces is presented using a laser line-scanning sensor. Due to registration errors, measurement errors, deformations, etc., a fast and accurate method is important in machine vision measurement. This paper builds a convenient and economic multi-view stereo (MVS) measurement system based on a linear stage and a rotary stage to reconstruct the measured object surface completely and accurately. In the proposed technique, the linear stage is used to generate the trigger signal and synchronize the laser sensor scanning; the rotary stage is used to rotate the object and obtain multi-view point cloud data, and then the multi-view point cloud data are registered and integrated into a 3D model. The measurement results show a measurement accuracy of 0.075 mm for a 360° reconstruction in 34 s, and some evaluation experiments were carried out to demonstrate the validity and practicability of the proposed technique.

Rules Research on Width of Pieces of Pants

2011 ◽  
Vol 215 ◽  
pp. 249-253
Author(s):  
B.F. Gu ◽  
J.Q. Su ◽  
H.Y. Kong ◽  
Guo Lian Liu
Keyword(s):  
Human Body ◽  
Measurement System ◽  
Point Cloud ◽  
Pattern Generation ◽  
Regression Equation ◽  
Body Measurement ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Body Scanning

. Based on 3D point-cloud data of human body, this paper probes rules research on width of pieces of pants. First, get the point-cloud figure of the studies through scanning the human body by 3D body scanning device (symcad). Read and optimize the point-cloud data by imageware software and obtain the total girth and the front/back girth of waist, abdomen, buttocks, thigh, knee and ankle. Then set the coefficients to establish the regression equation by using SPSS. Finally, verify the above-mentioned method through other studies to illustrate its feasibility. This study completes part of the work for the conversion from 3D garment pattern to 2D, to make up that the 2D non-contact body measurement system cannot directly obtain 3D sizes, and provides the basis for automatically pattern generation of pants.

scholarly journals Automatic Parametrization and Shadow Analysis of Roofs in Urban Areas from ALS Point Clouds with Solar Energy Purposes

2018 ◽  
Vol 7 (8) ◽  
pp. 301 ◽  
Author(s):  
Mario Soilán ◽  
Belén Riveiro ◽  
Patricia Liñares ◽  
Marta Padín-Beltrán
Keyword(s):  
Solar Energy ◽  
Urban Areas ◽  
Measurement Errors ◽  
Point Cloud ◽  
Smart Cities ◽  
Renewable Energy Sources ◽  
Point Clouds ◽  
Area Measurement ◽  
Point Cloud Data ◽  
Cloud Data

A basic feature of modern and smart cities is their energetic sustainability, using clean and renewable energies and, therefore, reducing the carbon emissions, especially in large cities. Solar energy is one of the most important renewable energy sources, being more significant in sunny climate areas such as the South of Europe. However, the installation of solar panels should be carried out carefully, being necessary to collect information about building roofs, regarding its surface and orientation. This paper proposes a methodology aiming to automatically parametrize building roofs employing point cloud data from an Aerial Laser Scanner (ALS) source. This parametrization consists of extracting not only the area and orientation of the roofs in an urban environment, but also of studying the shading of the roofs, given a date and time of the day. This methodology has been validated using 3D point cloud data of the city of Santiago de Compostela (Spain), achieving roof area measurement errors in the range of ±3%, showing that even low-density ALS data can be useful in order to carry out further analysis with energetic perspective.

The Application of Reverse Engineering in the Design of Electric Vehicle Panel

2009 ◽  
Vol 16-19 ◽  
pp. 283-287
Author(s):  
Feng Li ◽  
Lan Fang Feng ◽  
Xiao Qing Wu ◽  
Yan Bo Hui
Keyword(s):  
Reverse Engineering ◽  
Electric Vehicle ◽  
Point Cloud ◽  
Point Cloud Data ◽  
View Point ◽  
3D Design ◽  
Complex Curve ◽  
Cloud Data ◽  
Registration Algorithm ◽  
Light Measurement

Reverse engineering is a new modern product design technology, which is suitable for designing complex curve and surface, has been becoming a practical and widely-available engineering tool in panel design of automotive, motorcycle and electric vehicle. This paper first expatiates on the basic theory of structured light measurement system, registration algorithm of multi-view point cloud data and the basic principle of curve and surface reverse design, and then gives an application example in the 3D design of electric vehicle front panel.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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