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 Reconstruction of Conifer Root Systems Mapped with Point Cloud Data Obtained by 3D Laser Scanning Compared with Manual Measurement

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1117
Author(s):  
Chikage Todo ◽  
Hidetoshi Ikeno ◽  
Keitaro Yamase ◽  
Toko Tanikawa ◽  
Mizue Ohashi ◽  
...  
Keyword(s):  
Root System ◽  
Point Cloud ◽  
Laser Scanner ◽  
Root Systems ◽  
Point Cloud Data ◽  
Manual Measurement ◽  
Cloud Data ◽  
Accuracy Of Measurements ◽  
Laser Scanners ◽  
Point Data

Three-dimensional (3D) root system architecture (RSA) is a predominant factor in anchorage failure in trees. Only a few studies have used 3D laser scanners to evaluate RSA, but they do not check the accuracy of measurements. 3D laser scanners can quickly obtain RSA data, but the data are collected as a point cloud with a large number of points representing surfaces. The point cloud data must be converted into a set of interconnected axes and segments to compute the root system traits. The purposes of this study were: (i) to propose a new method for easily obtaining root point data as 3D coordinates and root diameters from point cloud data acquired by 3D laser scanner measurement; and (ii) to compare the accuracy of the data from main roots with intensive manual measurement. We scanned the excavated root systems of two Pinus thunbergii Parl. trees using a 3D laser scanner and neuTube software, which was developed for reconstructing the neuronal structure, to convert the point cloud data into root point data for reconstructing RSA. The reconstruction and traits of the RSA calculated from point cloud data were similar in accuracy to intensive manual measurements. Roots larger than 7 mm in diameter were accurately measured by the 3D laser scanner measurement. In the proposed method, the root point data were connected as a frustum of cones, so the reconstructed RSAs were simpler than the 3D root surfaces. However, the frustum of cones still showed the main coarse root segments correctly. We concluded that the proposed method could be applied to reconstruct the RSA and calculate traits using point cloud data of the root system, on the condition that it was possible to model both the stump and ovality of root sections.

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 EXPLOITING INDOOR MOBILE LASER SCANNER TRAJECTORIES FOR SEMANTIC INTERPRETATION OF POINT CLOUDS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 355-362 ◽  
Author(s):  
S. Nikoohemat ◽  
M. Peter ◽  
S. Oude Elberink ◽  
G. Vosselman
Keyword(s):  
Data Collection ◽  
Feature Detection ◽  
Laser Scanner ◽  
Point Clouds ◽  
Semantic Interpretation ◽  
Indoor Environments ◽  
Valuable Source ◽  
Laser Scanners ◽  
Occlusion Reasoning

The use of Indoor Mobile Laser Scanners (IMLS) for data collection in indoor environments has been increasing in the recent years. These systems, unlike Terrestrial Laser Scanners (TLS), collect data along a trajectory instead of at discrete scanner positions. In this research, we propose several methods to exploit the trajectories of IMLS systems for the interpretation of point clouds. By means of occlusion reasoning and use of trajectory as a set of scanner positions, we are capable of detecting openings in cluttered indoor environments. In order to provide information about both the partitioning of the space and the navigable space, we use the voxel concept for point clouds. Furthermore, to reconstruct walls, floor and ceiling we exploit the indoor topology and plane primitives. The results show that the trajectory is a valuable source of data for feature detection and understanding of indoor MLS point clouds.

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.

The Advantages of Using Laser Scanners in Surveying in Protected Sites

2015 ◽  
pp. 382-402
Author(s):  
Gülhan Benli
Keyword(s):  
Protected Areas ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Laser Scanners ◽  
Point Data

Since the 2000s, terrestrial laser scanning, as one of the methods used to document historical edifices in protected areas, has taken on greater importance because it mitigates the difficulties associated with working on large areas and saves time while also making it possible to better understand all the particularities of the area. Through this technology, comprehensive point data (point clouds) about the surface of an object can be generated in a highly accurate three-dimensional manner. Furthermore, with the proper software this three-dimensional point cloud data can be transformed into three-dimensional rendering/mapping/modeling and quantitative orthophotographs. In this chapter, the study will present the results of terrestrial laser scanning and surveying which was used to obtain three-dimensional point clouds through three-dimensional survey measurements and scans of silhouettes of streets in Fatih in Historic Peninsula in Istanbul, which were then transposed into survey images and drawings. The study will also cite examples of the facade mapping using terrestrial laser scanning data in Istanbul Historic Peninsula Project.

scholarly journals 3D SUPER-RESOLUTION APPROACH FOR SPARSE LASER SCANNER DATA

2015 ◽  
Vol II-3/W5 ◽  
pp. 151-157 ◽  
Author(s):  
S. Hosseinyalamdary ◽  
A. Yilmaz
Keyword(s):  
Anisotropic Diffusion ◽  
Low Cost ◽  
Laser Scanner ◽  
Super Resolution ◽  
Point Clouds ◽  
Superior Performance ◽  
Laser Scanners ◽  
Anisotropic Diffusion Equation ◽  
Tracking Object ◽  
High Level

Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.

scholarly journals Rule-based Classification of Airborne Laser Scanner Data for Automatic Extraction of 3D Objects in the Urban Area

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ngoc Quy BUI ◽  
Dinh Hien LE ◽  
Anh Quan DUONG ◽  
Quoc Long NGUYEN
Keyword(s):  
Land Use ◽  
Point Cloud ◽  
Laser Scanner ◽  
Building Detection ◽  
Land Use Classification ◽  
Close Point ◽  
Airborne Laser Scanner ◽  
Ndvi Index ◽  
Point Data ◽  
Point Cloud Classification

LiDAR technology has been widely adopted as a proper method for land cover classification.Recently with the development of technology, LiDAR systems can now capture high-resolutionmultispectral bands images with high-density LiDAR point cloud simultaneously. Therefore, it opens newopportunities for more precise automatic land-use classification methods by utilizing LiDAR data. Thisarticle introduces a combining technique of point cloud classification algorithms. The algorithms includeground detection, building detection, and close point classification - the classification is based on pointclouds’ attributes. The main attributes are heigh, intensity, and NDVI index calculated from 4 bands ofcolors extracted from multispectral images for each point. Data of the Leica City Mapper LiDAR systemin an area of 80 ha in Quang Xuong town, Thanh Hoa province, Vietnam was used to deploy theclassification. The data is classified into eight different types of land use consist of asphalt road, otherground, low vegetation, medium vegetation, high vegetation, building, water, and other objects. Theclassification workflow was implemented in the TerraSolid suite, with the result of the automation processcame out with 97% overall accuracy of classification points. The

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 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 3D RECONSTRUCTION OF THE <i>ROMAN DOMUS</i> IN THE ARCHAEOLOGICAL SITE OF <i>LYLIBAEUM</i> (MARSALA, ITALY)

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 437-442
Author(s):  
D. Ebolese ◽  
M. Lo Brutto ◽  
G. Dardanelli
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Archaeological Site ◽  
Technological Evolution ◽  
Laser Scanners ◽  
Scan Registration ◽  
High Level ◽  
Data Acquisition Strategy

<p><strong>Abstract.</strong> Generally, terrestrial laser scanning surveys involve a rather large number of scans to ensure a high percentage of overlap required for the scan registration phase (target-based or point-based registration, cloud-to-cloud registration). These approaches result in data redundancy that could slow down both the acquisition and post-processing phases. In recent years, the technological evolution in the field of laser scanners has been directed to the development of devices that are able to perform an onsite pre-registration, to optimize the survey procedures and the reliability of the registration of the scan. The paper presents the results achieved during a terrestrial laser scanning survey carried out for the documentation and 3D reconstruction of the large and complex archaeological remains of the so-called Roman <i>Domus</i> in the archaeological site of <i>Lylibaeum</i> (Marsala, Italy). The survey was also conducted using a terrestrial laser scanner capable of pre-registering scans using a topographic approach. The pre-registration procedure and the data acquisition strategy have allowed to optimize the workflow and to obtain a 3D model of the Roman <i>Domus</i> with a high level of detail and area coverage.</p>

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