Scanning and Modeling of Large Thin-Walled Curved Surface Part

2011 ◽  
Vol 299-300 ◽  
pp. 810-815 ◽  
Author(s):  
Chun Wang ◽  
Xuan Ming Zhang ◽  
Xiao Wang
Keyword(s):  
Sandwich Structure ◽  
Laser Scanner ◽  
Welding Process ◽  
Point Clouds ◽  
Cnc Machining ◽  
Curved Surface ◽  
Surface Model ◽  
Forming Process ◽  
Thin Walled ◽  
Sandwich Core

The large sandwich structure composed of thin-walled aluminum alloy panels, and variable thickness of honeycomb or Polymethacrylimide (PMI) foam core is usually manufactured by pre-bonded forming process, that is pre-forming panels and sandwich core, and then curing adhesive them to be sandwich structure. Welding process of large thin-walled panel causes the panel surface to be irregular and have greater errors relative to the design surface. Simply CNC machining the sandwich core according to the design surface cannot guarantee an exact match sandwich core consistent with the panels. The actual topography of the panels must be scanned. It is proposed that the use of a new hand-held laser scanner, Handyscan to scan large thin-walled curved surface parts, of Geomagic software to handle the acquired point clouds and construct the surface model.

Manufacturing Process of Large Scale Sandwich Structure with Variable Thickness of PMI Foam Core

2011 ◽  
Vol 299-300 ◽  
pp. 816-819
Author(s):  
Chun Wang ◽  
Xuan Ming Zhang ◽  
Chun Ying Tang
Keyword(s):  
Aluminum Alloy ◽  
Variable Thickness ◽  
Sandwich Structure ◽  
Large Scale ◽  
Cnc Machining ◽  
Foam Core ◽  
Molding Process ◽  
Thin Walled ◽  
Sandwich Core ◽  
Aluminum Alloy Panels

Composite sandwich structures are extensively used in the aerospace, wind power, sports equipment, shipbuilding, automotive and train locomotive industries in order to improve structure rigidity and reduce weight. The molding process of sandwich structure using glass cloth and fibre materials as panels has been reported in many literatures. However, few researches are found relative to the molding process of large scale sandwich structure with the characteristics of thin-walled aluminum alloy panels and variable thickness of Polymethacrylimide (PMI) foam cores. This paper describes a preformed molding process that consists of thermoforming foam core blocks, assembling blocks into a whole sandwich core, CNC machining the sandwich core according to surface models of the thin-walled aluminum alloy panels, and finally, bonding and curing panels and sandwich cores.

scholarly journals POTENTIAL OF FULL WAVEFORM AIRBORNE LASER SCANNING DATA FOR URBAN AREA CLASSIFICATION – TRANSFER OF CLASSIFICATION APPROACHES BETWEEN MISSIONS

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1317-1323 ◽  
Author(s):  
G. Tran ◽  
D. Nguyen ◽  
M. Milenkovic ◽  
N. Pfeifer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Surface Model ◽  
Knowledge Based ◽  
Full Waveform ◽  
Airborne Laser ◽  
Point Density ◽  
Area Classification

Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

scholarly journals As-Textured As-Built BIM Using Sensor Fusion, Zee Ain Historical Village as a Case Study

2021 ◽  
Vol 13 (24) ◽  
pp. 5135
Author(s):  
Yahya Alshawabkeh ◽  
Ahmad Baik ◽  
Ahmad Fallatah
Keyword(s):  
Cultural Heritage ◽  
Laser Scanner ◽  
Texture Mapping ◽  
Point Clouds ◽  
Data Interpretation ◽  
Optimal Time ◽  
Surface Model ◽  
Image Capture ◽  
Heritage Site ◽  
Point Data

The work described in the paper emphasizes the importance of integrating imagery and laser scanner techniques (TLS) to optimize the geometry and visual quality of Heritage BIM. The fusion-based workflow was approached during the recording of Zee Ain Historical Village in Saudi Arabia. The village is a unique example of traditional human settlements, and represents a complex natural and cultural heritage site. The proposed workflow divides data integration into two levels. At the basic level, UAV photogrammetry with enhanced mobility and visibility is used to map the ragged terrain and supplement TLS point data in upper and unaccusable building zones where shadow data originated. The merging of point clouds ensures that the building’s overall geometry is correctly rebuilt and that data interpretation is improved during HBIM digitization. In addition to the correct geometry, texture mapping is particularly important in the area of cultural heritage. Constructing a realistic texture remains a challenge in HBIM; because the standard texture and materials provided in BIM libraries do not allow for reliable representation of heritage structures, mapping and sharing information are not always truthful. Thereby, at the second level, the workflow proposed true orthophoto texturing method for HBIM models by combining close-range imagery and laser data. True orthophotos have uniform scale that depicts all objects in their respective planimetric positions, providing reliable and realistic mapping. The process begins with the development of a Digital Surface Model (DSM) by sampling TLS 3D points in a regular grid, with each cell uniquely associated with a model point. Then each DSM cell is projected in the corresponding perspective imagery in order to map the relevant spectral information. The methods allow for flexible data fusion and image capture using either a TLS-installed camera or a separate camera at the optimal time and viewpoint for radiometric data. The developed workflows demonstrated adequate results in terms of complete and realistic textured HBIM, allowing for a better understanding of the complex heritage structures.

scholarly journals COMPARISON ASSESSMENT OF DIGITAL 3D MODELS OBTAINED BY DRONE-BASED LIDAR AND DRONE IMAGERY

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 113-118
Author(s):  
M. Bouziani ◽  
M. Amraoui ◽  
S. Kellouch
Keyword(s):  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
3D Models ◽  
Pulse Frequency ◽  
Airborne Lidar ◽  
Measurement Unit ◽  
Surface Model ◽  
Digital Products

Abstract. The purpose of this study is to assess the potential of drone airborne LiDAR technology in Morocco in comparison with drone photogrammetry. The cost and complexity of the equipment which includes a laser scanner, an inertial measurement unit, a positioning system and a platform are among the causes limiting its use. Furthermore, this study was motivated by the following reasons: (1) Limited number of studies in Morocco on drone-based LiDAR technology applications, (2) Lack of study on the parameters that influence the quality of drone-based LiDAR surveys as well as on the evaluation of the accuracy of derived products. In this study, the evaluation of LiDAR technology was carried out by an analysis of the geometric accuracy of the 3D products generated: Digital Terrain Model (DTM), Digital Surface Model (DSM) and Digital Canopy Model (DCM). We conduct a comparison with the products generated by drone photogrammetry and GNSS surveys. Several tests were carried out to analyse the parameters that influence the mission results namely height, overlap, drone speed and laser pulse frequency. After data collection, the processing phase was carried out. It includes: the cleaning, the consolidation then the classification of point clouds and the generation of the various digital models. This project also made it possible to propose and validate a workflow for the processing, the classification of point clouds and the generation of 3D digital products derived from the processing of LiDAR data acquired by drone.

scholarly journals Strategies for 3D Modelling of Buildings from Airborne Laser Scanner and Photogrammetric Data Based on Free-Form and Model-Driven Methods: The Case Study of the Old Town Centre of Bordeaux (France)

2021 ◽  
Vol 11 (22) ◽  
pp. 10993
Author(s):  
Domenica Costantino ◽  
Gabriele Vozza ◽  
Vincenzo Saverio Alfio ◽  
Massimiliano Pepe
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Free Form ◽  
Surface Model ◽  
Parametric Modelling ◽  
Model Driven ◽  
Town Centre

This paper presents a data-driven free-form modelling method dedicated to the parametric modelling of buildings with complex shapes located in particularly valuable Old Town Centres, using Airborne LiDAR Scanning (ALS) data and aerial imagery. The method aims to reconstruct and preserve the input point cloud based on the relative density of the data. The method is based on geometric operations, iterative transformations between point clouds, meshes, and shape identification. The method was applied on a few buildings located in the Old Town Centre of Bordeaux (France). The 3D model produced shows a mean distance to the point cloud of 0.058 m and a standard deviation of 0.664 m. In addition, the incidence of building footprint segmentation techniques in automatic and interactive model-driven modelling was investigated and, in order to identify the best approach, six different segmentation methods were tested. The segmentation was performed based on the footprints derived from Digital Surface Model (DSM), point cloud, nadir images, and OpenStreetMap (OSM). The comparison between the models shows that the segmentation that produces the most accurate and precise model is the interactive segmentation based on nadir images. This research also shows that in modelling complex structures, the model-driven method can achieve high levels of accuracy by including an interactive editing phase in building 3D models.

scholarly journals COMPARISON OF UAV IMAGERY-DERIVED POINT CLOUD TO TERRESTRIAL LASER SCANNER POINT CLOUD

2019 ◽  
Vol IV-2/W5 ◽  
pp. 149-155 ◽  
Author(s):  
S. Peterson ◽  
J. Lopez ◽  
R. Munjy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Focal Length ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Surface Model ◽  
Control Points ◽  
Ground Control Points ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> A small unmanned aerial vehicle (UAV) with survey-grade GNSS positioning is used to produce a point cloud for topographic mapping and 3D reconstruction. The objective of this study is to assess the accuracy of a UAV imagery-derived point cloud by comparing a point cloud generated by terrestrial laser scanning (TLS). Imagery was collected over a 320&amp;thinsp;m by 320&amp;thinsp;m area with undulating terrain, containing 80 ground control points. A SenseFly eBee Plus fixed-wing platform with PPK positioning with a 10.6&amp;thinsp;mm focal length and a 20&amp;thinsp;MP digital camera was used to fly the area. Pix4Dmapper, a computer vision based commercial software, was used to process a photogrammetric block, constrained by 5 GCPs while obtaining cm-level RMSE based on the remaining 75 checkpoints. Based on results of automatic aerial triangulation, a point cloud and digital surface model (DSM) (2.5&amp;thinsp;cm/pixel) are generated and their accuracy assessed. A bias less than 1 pixel was observed in elevations from the UAV DSM at the checkpoints. 31 registered TLS scans made up a point cloud of the same area with an observed horizontal root mean square error (RMSE) of 0.006m, and negligible vertical RMSE. Comparisons were made between fitted planes of extracted roof features of 2 buildings and centreline profile comparison of a road in both UAV and TLS point clouds. Comparisons showed an average +8&amp;thinsp;cm bias with UAV point cloud computing too high in two features. No bias was observed in the roof features of the southernmost building.</p>

Integration of point clouds from UAV photogrammetry and laserscan survey for the assessment of the risk of collapse of the vault of an underground cavity

2020 ◽  
Author(s):  
Davide Martinucci ◽  
Simone Pillon ◽  
Annelore Bezzi ◽  
Giulia Casagrande ◽  
Giorgio Fontolan ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Control Point ◽  
Laser Scanner ◽  
Point Clouds ◽  
Integrated Approach ◽  
Surface Model ◽  
North East ◽  
Critical Issues ◽  
Gps Rtk

&lt;p&gt;Photogrammetric surveys from UAV and LiDAR surveys are two techniques that allow for the production of very high resolution point clouds. The use of these techniques result in a detailed reconstruction of difficult-to-access environments such as underground cavities. A rigorous georeferencing of the acquired data allows for a comparison of the hypogean development of the cave to the overlying territory. This study presents a case of integration between these two techniques, applied to the risk assessment of the collapse of the vaults in a natural cavity in the Trieste Karst (north east Italy). This site is particularly delicate given that on the slope above the cave there is an abandoned stone quarry. In order to survey the quarry above the cave, a flight was performed with UAV, while the cave was surveyed with Laser Scan from the ground. The flight was made using a UAV DJI Phantom RTK, which carried a 20 Mpixel 1&amp;#8220; sensor camera. 8 ha of terrain was surveyed, capturing about 733 high resolution images and surveying 22 GCPs (Ground Control Point) with a GNSS RTK receiver. It was possible to reduce the number of GCPs, since the drone recorded the shooting positions very accurately with the on-board GPS RTK. Data were analyzed using Agisoft Metashape Professional to produce an orthophoto and a DSM (Digital Surface Model) with a ground resolution of 0.02 m and 0.04 m respectively. The point cloud has a density of 586 points/m&lt;sup&gt;2&lt;/sup&gt;. The LiDaR survey was carried out using an ILRIS 3D ER laser scanner from Optec. The point cloud has a density of approximately 2500 points/m&lt;sup&gt;2&lt;/sup&gt; and 5 stations were needed to cover the underground development of the cavity. The georeferencing of the data was carried out by roto-translation on geo-referenced benchmarks, surveyed with GPS RTK and total station. The point cloud was processed using Terrascan software (Terrasolid). The two point clouds were aligned, geo-referenced and combined using Polyworks software (Innovmetric), in order to check the thicknesses of the material present above the vault of the cave. The integration of epigean and hypogean data made it possible to identify some critical points related to a vault thickness of approximately 1.5 meters, located at the quarry square. This work made it possible to highlight critical issues difficult to detect without the integrated approach of these different survey methodologies.&lt;/p&gt;

scholarly journals Fitting Terrestrial Laser Scanner Point Clouds with T-Splines: Local Refinement Strategy for Rigid Body Motion

2021 ◽  
Vol 13 (13) ◽  
pp. 2494
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Point Cloud ◽  
Goodness Of Fit ◽  
Laser Scanner ◽  
Point Clouds ◽  
Statistical Testing ◽  
Computational Time ◽  
Terrestrial Laser Scanner ◽  
Reference Surface ◽  
Local Refinement ◽  
Surface Approximation

T-splines have recently been introduced to represent objects of arbitrary shapes using a smaller number of control points than the conventional non-uniform rational B-splines (NURBS) or B-spline representatizons in computer-aided design, computer graphics and reverse engineering. They are flexible in representing complex surface shapes and economic in terms of parameters as they enable local refinement. This property is a great advantage when dense, scattered and noisy point clouds are approximated using least squares fitting, such as those from a terrestrial laser scanner (TLS). Unfortunately, when it comes to assessing the goodness of fit of the surface approximation with a real dataset, only a noisy point cloud can be approximated: (i) a low root mean squared error (RMSE) can be linked with an overfitting, i.e., a fitting of the noise, and should be correspondingly avoided, and (ii) a high RMSE is synonymous with a lack of details. To address the challenge of judging the approximation, the reference surface should be entirely known: this can be solved by printing a mathematically defined T-splines reference surface in three dimensions (3D) and modeling the artefacts induced by the 3D printing. Once scanned under different configurations, it is possible to assess the goodness of fit of the approximation for a noisy and potentially gappy point cloud and compare it with the traditional but less flexible NURBS. The advantages of T-splines local refinement open the door for further applications within a geodetic context such as rigorous statistical testing of deformation. Two different scans from a slightly deformed object were approximated; we found that more than 40% of the computational time could be saved without affecting the goodness of fit of the surface approximation by using the same mesh for the two epochs.

scholarly journals Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 59
Author(s):  
Gaël Kermarrec ◽  
Niklas Schild ◽  
Jan Hartmann
Keyword(s):  
Least Squares ◽  
Laser Scanner ◽  
Real Data ◽  
Point Clouds ◽  
Statistical Testing ◽  
Normal Vector ◽  
Engineering Structures ◽  
3D Point Clouds ◽  
Least Squares Adjustment ◽  
Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

scholarly journals LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2263
Author(s):  
Haileleol Tibebu ◽  
Jamie Roche ◽  
Varuna De Silva ◽  
Ahmet Kondoz
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Test Results ◽  
New Approach ◽  
Occupancy Grid ◽  
Laser Scanners ◽  
Novel Method ◽  
Occupancy Grid Mapping ◽  
Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

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