scholarly journals Pre-Processing and Surface Reconstruction of Points Cloud Based on Chord Angle Algorithm Technique

2020 ◽  
Vol 16 (3) ◽  
pp. 34-42
Author(s):  
Ali M. Albdairy ◽  
Ahmed A. A. Al-Duroobi ◽  
Maan A. Tawfiq
Keyword(s):  
Case Studies ◽  
Point Cloud ◽  
Rapid Development ◽  
Laser Scanner ◽  
Modern Technology ◽  
Mathematical Representation ◽  
Adjacent Pair ◽  
3D Objects ◽  
Laser Scanners ◽  
Data Points

Abstract Although the rapid development in reverse engineering techniques, 3D laser scanners can be considered the modern technology used to digitize the 3D objects, but some troubles may be associate this process due to the environmental noises and limitation of the used scanners. So, in the present paper a data pre-processing algorithm has been proposed to obtain the necessary geometric features and mathematical representation of scanned object from its point cloud which obtained using 3D laser scanner (Matter and Form) through isolating the noised points. The proposed algorithm based on continuous calculations of chord angle between each adjacent pair of points in point cloud. A MATLAB program has been built to perform the proposed algorithm which implemented using a suggested case studies with cylinder and dome shape. The resulted point cloud from application the proposed algorithm and result of surface fitting for the case studies has been proved the proficiency of the proposed chord angle algorithm in pre-processing of data points and clean the point cloud, where the percent of data which was ignored as noisy data points according to proposed chord angle algorithm was arrived to (81.52%) and (75.01%)of total number of data points in point cloud for first and second case study respectively.

scholarly journals POINTS CLOUD PRE-PROCESSING AND SAMPLING BASED ON DISTANCE ALGORITHM TECHNIQUE

2021 ◽  
Vol 25 (02) ◽  
pp. 1-8
Author(s):  
Ali M. Al-Bdairy ◽  
◽  
Ahmed A. A. Al-Duroobi ◽  
Maan A. Tawfiq ◽  
◽  
...  
Keyword(s):  
Rapid Development ◽  
Laser Scanner ◽  
Mathematical Representation ◽  
Surface Model ◽  
Sampling Process ◽  
Huge Data ◽  
Geometrical Features ◽  
Laser Scanners ◽  
Sampling Algorithms

Although the rapid development of reverse engineering techniques such as a modern 3D laser scanners, but can’t use this techniques immediately to generate a perfect surface model for the scanned parts, due to the huge data, the noisy data which associated to the scanning process, and the accuracy limitation of some scanning devices, so, the present paper present a points cloud pre-processing and sampling algorithms have been proposed based on distance calculations and statistical considerations to simplify the row points cloud which obtained using MATTER and FORM 3D laser scanner as a manner to obtain the required geometrical features and mathematical representation from the row points cloud of the scanned object through detection, isolating, and deleting the noised points. A MATLAB program has been constructed for executing the proposed algorithms implemented using a suggested case study with non-uniform shape. The results were proved the validity of the introduced distance algorithms for pre-processing and sampling process where the proficiency percent for pre-processing was (18.65%) with a single attempt, and the counted deviation value rang with the sampling process was (0.0002-0.3497mm).

Point Clouds Pre-Processing and Surface Reconstruction Based on Tangent Continuity Algorithm Technique

2020 ◽  
Vol 38 (6A) ◽  
pp. 917-925
Author(s):  
Ali M. Al-Bdairy ◽  
Ahmed A.A. Al-Duroobi ◽  
Maan A. Tawfiq
Keyword(s):  
Case Studies ◽  
Surface Reconstruction ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Objects ◽  
Data Points ◽  
3D Scanners ◽  
Different Shapes ◽  
Noise Factors

Pre-processing is essential for processing the row data point clouds which acquired using a 3D laser scanner as a modern technique to digitize and reconstruct the surface of the 3D objects in reverse engineering applications. Due to the accuracy limitation of some 3D scanners and the environmental noise factors such as illumination and reflection, there are some noised data points associated with the row point clouds, so, in the present paper, a preprocessing algorithm has been proposed to determine and delete the unnecessary data as noised points and save the remaining data points for the surface reconstruction of 3D objects from its point clouds which acquired using the 3D laser scanner (Matter and Form). The proposed algorithm based on the assessment of tangent continuity as a geometrical feature and criteria for the contiguous points. A MATLAB software has been used to construct a program for the proposed point clouds pre-processing algorithm, the validity of the constructed program has been proved using geometrical case studies with different shapes. The application results of the proposed tangent algorithm and surface fitting process for the suggested case studies were proved the validity of the proposed algorithm for simplification of the point clouds, where the percent of noised data which removed according to the proposed tangent continuity algorithm which achieved a reduction of the total points to a percentage of (43.63%), and (32.01%) for the studied case studies, from the total number of data points in point cloud for first and second case study respectively.

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals PRECISE INDOOR LOCALIZATION FOR MOBILE LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 1-6 ◽  
Author(s):  
R. Kaijaluoto ◽  
A. Hyyppä
Keyword(s):  
Data Collection ◽  
Point Cloud ◽  
Indoor Localization ◽  
Collection Efficiency ◽  
Laser Scanner ◽  
Functional Method ◽  
Single View ◽  
Laser Scanners ◽  
Point Data ◽  
High Level

Accurate 3D data is of high importance for indoor modeling for various applications in construction, engineering and cultural heritage documentation. For the lack of GNSS signals hampers use of kinematic platforms indoors, TLS is currently the most accurate and precise method for collecting such a data. Due to its static single view point data collection, excessive time and data redundancy are needed for integrity and coverage of data. However, localization methods with affordable scanners are used for solving mobile platform pose problem. The aim of this study was to investigate what level of trajectory accuracies can be achieved with high quality sensors and freely available state of the art planar SLAM algorithms, and how well this trajectory translates to a point cloud collected with a secondary scanner. <br><br> In this study high precision laser scanners were used with a novel way to combine the strengths of two SLAM algorithms into functional method for precise localization. We collected five datasets using Slammer platform with two laser scanners, and processed them with altogether 20 different parameter sets. The results were validated against TLS reference. The results show increasing scan frequency improves the trajectory, reaching 20 mm RMSE levels for the best performing parameter sets. Further analysis of the 3D point cloud showed good agreement with TLS reference with 17 mm positional RMSE. With precision scanners the obtained point cloud allows for high level of detail data for indoor modeling with accuracies close to TLS at best with vastly improved data collection efficiency.

scholarly journals Reality Capture of Buildings Using 3D Laser Scanners

2021 ◽  
Author(s):  
Avar Almukhtar ◽  
Henry Abanda ◽  
Zaid O. Saeed ◽  
Joseph H.M. Tah
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Construction Project ◽  
Laser Scanner ◽  
Data File ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Project Data

The urgent need to improve performance in the construction industry has led to the adoption of many innovative technologies. 3D laser scanners are amongst the leading technologies being used to capture and process assets or construction project data for use in various applications. Due to its nascent nature, many questions are still unanswered about 3D laser scanning, which in turn contribute to the slow adaptation of the technology. Some of these include the role of 3D laser scanners in capturing and processing raw construction project data. How accurate is the 3D laser scanner or point cloud data? How does laser scanning fit with other wider emerging technologies such as Building Information Modelling (BIM)? This study adopts a proof-of-concept approach, which in addition to answering the afore-mentioned questions, illustrates the application of the technology in practice. The study finds that the quality of the data, commonly referred to as point cloud data is still a major issue as it depends on the distance between the target object and 3D laser scanner’s station. Additionally, the quality of the data is still very dependent on data file sizes and the computational power of the processing machine. Lastly, the connection between laser scanning and BIM approaches is still weak as what can be done with a point cloud data model in a BIM environment is still very limited. The aforementioned findings reinforce existing views on the use of 3D laser scanners in capturing and processing construction project data.

Automated Part Inspection Using 3D Point Clouds

2013 ◽  
Author(s):  
Lee J. Wells ◽  
Mohammed S. Shafae ◽  
Jaime A. Camelio
Keyword(s):  
Point Cloud ◽  
Manufacturing Systems ◽  
Ad Hoc ◽  
Point Clouds ◽  
Measurement Technology ◽  
False Alarms ◽  
3D Point Clouds ◽  
Laser Scanners ◽  
Data Points ◽  
Part Inspection

Ever advancing sensor and measurement technologies continually provide new opportunities for knowledge discovery and quality control (QC) strategies for complex manufacturing systems. One such state-of-the-art measurement technology currently being implemented in industry is the 3D laser scanner, which can rapidly provide millions of data points to represent an entire manufactured part’s surface. This gives 3D laser scanners a significant advantage over competing technologies that typically provide tens or hundreds of data points. Consequently, data collected from 3D laser scanners have a great potential to be used for inspecting parts for surface and feature abnormalities. The current use of 3D point clouds for part inspection falls into two main categories; 1) Extracting feature parameters, which does not complement the nature of 3D point clouds as it wastes valuable data and 2) An ad-hoc manual process where a visual representation of a point cloud (usually as deviations from nominal) is analyzed, which tends to suffer from slow, inefficient, and inconsistent inspection results. Therefore our paper proposes an approach to automate the latter approach to 3D point cloud inspection. The proposed approach uses a newly developed adaptive generalized likelihood ratio (AGLR) technique to identify the most likely size, shape, and magnitude of a potential fault within the point cloud, which transforms the ad-hoc visual inspection approach to a statistically viable automated inspection solution. In order to aid practitioners in designing and implementing an AGLR-based inspection process, our paper also reports the performance of the AGLR with respect to the probability of detecting specific size and magnitude faults in addition to the probability of a false alarms.

scholarly journals Registration of time of flight terrestrial laser scanner data for stop-and-go mode

2014 ◽  
Vol XL-1 ◽  
pp. 287-291
Author(s):  
H. Mohammed ◽  
N. M. Alsubaie ◽  
M. Elhabiby ◽  
N. El-sheimy
Keyword(s):  
Long Range ◽  
Point Cloud ◽  
Laser Scanner ◽  
Time Of Flight ◽  
Initial Approximation ◽  
Continuous Mode ◽  
Mobile Mapping ◽  
Stop And Go ◽  
Laser Scanners ◽  
Cloud Density

Terrestrial Laser Scanners (TLS) are utilized through different data acquisition techniques such as Mobile Laser Scanning (MLS) and the output can be used in different applications such as 3D city modelling, cultural heritage documentations, oil and Gas as built, etc... In this research paper, we will investigate one of the modes of TLS on mobile mapping platform. Namely the Stop-and-Go (SAG) mode. Unlike the continuous mode, the Stop-and-Go mode does not require the use of IMU to estimate the TLS attitude and thus inturn it has an overall reduction in the system cost. Moreover, it decreases the time required for data processing in comparison with the continuous mode. For successful use of SAG mobile mapping in urban areas, it is preferred to use a long range time of flight laser scanner to cover long distances in each scan and minimize the registration error. The problem arise with Long range laser scanners is their low point cloud density. The low point cloud density affects the registration accuracy specially in monitoring applications. The point spacing between points is one of the issues facing the registration especially when the matching points are chosen manually. <br><br> Since most of TLS nowadays are equipped with camera on-board we can utilize the camera to get an initial estimate of the registration parameters based on image matching. After having an initial approximation of the registration parameters we feed those parameters to the Iterative Closest Point algorithm to obtain more accurate registration result.

scholarly journals DRAWING AND LANDSCAPE SIMULATION FOR JAPANESE GARDEN BY USING TERRESTRIAL LASER SCANNER

2015 ◽  
Vol XL-4/W5 ◽  
pp. 233-238
Author(s):  
R. Kumazaki ◽  
Y. Kunii
Keyword(s):  
Point Cloud ◽  
Landscape Architecture ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Contour Lines ◽  
Japanese Garden ◽  
Laser Scanners

Recently, many laser scanners are applied for various measurement fields. This paper investigates that it was useful to use the terrestrial laser scanner in the field of landscape architecture and examined a usage in Japanese garden. As for the use of 3D point cloud data in the Japanese garden, it is the visual use such as the animations. Therefore, some applications of the 3D point cloud data was investigated that are as follows. Firstly, ortho image of the Japanese garden could be outputted for the 3D point cloud data. Secondly, contour lines of the Japanese garden also could be extracted, and drawing was became possible. Consequently, drawing of Japanese garden was realized more efficiency due to achievement of laborsaving. Moreover, operation of the measurement and drawing could be performed without technical skills, and any observers can be operated. Furthermore, 3D point cloud data could be edited, and some landscape simulations that extraction and placement of tree or some objects were became possible. As a result, it can be said that the terrestrial laser scanner will be applied in landscape architecture field more widely.

Dealing with systematic laser scanner errors due to misalignment at area-based deformation analyses

2018 ◽  
Vol 12 (2) ◽  
pp. 169-185 ◽  
Author(s):  
Christoph Holst ◽  
Tomislav Medić ◽  
Heiner Kuhlmann
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Bundle Adjustment ◽  
Deformation Analysis ◽  
Random Errors ◽  
3D Data ◽  
In Situ Calibration ◽  
Laser Scanners ◽  
Main Reflector

Abstract The ability to acquire rapid, dense and high quality 3D data has made terrestrial laser scanners (TLS) a desirable instrument for tasks demanding a high geometrical accuracy, such as geodetic deformation analyses. However, TLS measurements are influenced by systematic errors due to internal misalignments of the instrument. The resulting errors in the point cloud might exceed the magnitude of random errors. Hence, it is important to assure that the deformation analysis is not biased by these influences. In this study, we propose and evaluate several strategies for reducing the effect of TLS misalignments on deformation analyses. The strategies are based on the bundled in-situ self-calibration and on the exploitation of two-face measurements. The strategies are verified analyzing the deformation of the Onsala Space Observatory’s radio telescope’s main reflector. It is demonstrated that either two-face measurements as well as the in-situ calibration of the laser scanner in a bundle adjustment improve the results of deformation analysis. The best solution is gained by a combination of both strategies.

scholarly journals Space Subdivision of Indoor Mobile Laser Scanning Data Based on the Scanner Trajectory

2018 ◽  
Vol 10 (11) ◽  
pp. 1815 ◽  
Author(s):  
Ahmed Elseicy ◽  
Shayan Nikoohemat ◽  
Michael Peter ◽  
Sander Oude Elberink
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Information ◽  
State Of The Art ◽  
Laser Scanner ◽  
Indoor Space ◽  
Laser Scanners ◽  
Real World Datasets ◽  
Indoor Spaces

State-of-the-art indoor mobile laser scanners are now lightweight and portable enough to be carried by humans. They allow the user to map challenging environments such as multi-story buildings and staircases while continuously walking through the building. The trajectory of the laser scanner is usually discarded in the analysis, although it gives insight about indoor spaces and the topological relations between them. In this research, the trajectory is used in conjunction with the point cloud to subdivide the indoor space into stories, staircases, doorways, and rooms. Analyzing the scanner trajectory as a standalone dataset is used to identify the staircases and to separate the stories. Also, the doors that are traversed by the operator during the scanning are identified by processing only the interesting spots of the point cloud with the help of the trajectory. Semantic information like different space labels is assigned to the trajectory based on the detected doors. Finally, the point cloud is semantically enriched by transferring the labels from the annotated trajectory to the full point cloud. Four real-world datasets with a total of seven stories are used to evaluate the proposed methods. The evaluation items are the total number of correctly detected rooms, doors, and staircases.

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