Obtaining as-built models of manufacturing plants from point clouds

2018 ◽  
Vol 66 (5) ◽  
pp. 397-405 ◽  
Author(s):  
Jochen Meidow ◽  
Thomas Usländer ◽  
Karsten Schulz
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Relevant Information ◽  
Manufacturing Plant ◽  
Manufacturing Plants ◽  
Digital Twins ◽  
Simulation Based ◽  
Specific Object ◽  
Degree Of Maturity ◽  
Object Models

Abstract The capability to adapt a manufacturing plant to changing requirements gains increasing importance in industrial production environments, e. g., triggered by Industrie 4.0 scenarios. A virtual as-built model of a manufacturing plant and its surrounding factory building provides important decision support and relevant information for digital twins, e. g., to trace assets and asset types across their whole lifetime, planning of renovations, plant and machine topology changes, or the simulation-based analysis of production processes. Based on point clouds obtained by terrestrial laser scanning or photogrammetric acquisition, reverse engineering can be applied to extract and to reconstruct relevant objects in a form suitable for CAD programs. In this article, we review approaches to capture a scene by point measurements and to reconstruct the geometry of its components given specific object models. This comprises the discussion of various representation schemes for objects and their relations, strategies for object recognition, and the explication of methods for model instantiation. Furthermore, depending on the requirements for specific tasks, we identify technology gaps and specify the degree of maturity of the related techniques.

scholarly journals Technical Note: On the Production and Accuracy of CNC-Manufactured Hydraulic Scale Models

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 916
Author(s):  
Jochen Aberle ◽  
Ralph Eikenberg ◽  
Till Branß ◽  
Pierre-Yves Henry
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Technical Note ◽  
Production Technique ◽  
Statistical Parameters ◽  
Digital Twins ◽  
Scale Models ◽  
Computer Numerically Controlled ◽  
High Degree

This Technical Note addresses the workflow for the production of hydraulic scale models using a Computer Numerically Controlled (CNC) production technique and investigates the possibilities to accurately reproduce topographical roughness features. Focusing on the construction of three scale models of unlined rock blasted tunnels, their accuracy is evaluated based on the comparison of differences between scaled prototype point clouds obtained by terrestrial laser scanning, spatially filtered meshes that served as input for the milling of the models, and digital twins of the constructed models that were created by Structure from Motion photogrammetry. The direct comparison between the point clouds and meshes as well as the comparison of derived statistical parameters show that the models could be reproduced with a high degree of accuracy. Observed deviations between the point clouds of the milled models and the milling meshes, as well as the scaled original point cloud, are identified and discussed in light of the production technique and the accuracy of the applied methods for the comparison.

scholarly journals AUTOMATIC 3D BUILDINGS COMPACT RECONSTRUCTION FROM LIDAR POINT CLOUDS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 473-478
Author(s):  
G.-A. Nys ◽  
R. Billen ◽  
F. Poux
Keyword(s):  
Large Scale ◽  
Point Clouds ◽  
Relevant Information ◽  
Heat Radiation ◽  
Direct Extraction ◽  
Real World Data ◽  
Radiation Model ◽  
Digital Twins ◽  
Potential Use ◽  
City Models

Abstract. Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids).

scholarly journals Quality Evaluation of Digital Twins Generated Based on UAV Photogrammetry and TLS: Bridge Case Study

2021 ◽  
Vol 13 (17) ◽  
pp. 3499
Author(s):  
Masoud Mohammadi ◽  
Maria Rashidi ◽  
Vahid Mousavi ◽  
Ali Karami ◽  
Yang Yu ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Geometric Accuracy ◽  
Digital Twins ◽  
Surface Deviation ◽  
Noise Data ◽  
Bridge Inspections ◽  
Aerial Vehicle ◽  
Comparative Results

In the current modern era of information and technology, emerging remote advancements have been widely established for detailed virtual inspections and assessments of infrastructure assets, especially bridges. These technologies are capable of creating an accurate digital representation of the existing assets, commonly known as the digital twins. Digital twins are suitable alternatives to in-person and on-site based assessments that can provide safer, cheaper, more reliable, and less distributive bridge inspections. In the case of bridge monitoring, Unmanned Aerial Vehicle (UAV) photogrammetry and Terrestrial Laser Scanning (TLS) are among the most common advanced technologies that hold the potential to provide qualitative digital models; however, the research is still lacking a reliable methodology to evaluate the generated point clouds in terms of quality and geometric accuracy for a bridge size case study. Therefore, this paper aims to provide a comprehensive methodology along with a thorough bridge case study to evaluate two digital point clouds developed from an existing Australian heritage bridge via both UAV-based photogrammetry and TLS. In this regard, a range of proposed approaches were employed to compare point clouds in terms of points’ distribution, level of outlier noise, data completeness, surface deviation, and geometric accuracy. The comparative results of this case study not only proved the capability and applicability of the proposed methodology and approaches in evaluating these two voluminous point clouds, but they also exhibited a higher level of point density and more acceptable agreements with as-is measurements in TLS-based point clouds subjected to the implementation of a precise data capture and a 3D reconstruction model.

scholarly journals Comparison Of Laser Scanning And Photogrammetry And Their Use For Digital Recording Of Cultural Monument Case Study: Byzantine Land Walls-Istanbul

2015 ◽  
Vol II-5/W3 ◽  
pp. 17-24 ◽  
Author(s):  
B. Bayram ◽  
G. Nemli ◽  
T. Özkan ◽  
O. E. Oflaz ◽  
B. Kankotan ◽  
...  
Keyword(s):  
3D Modeling ◽  
Laser Scanning ◽  
Point Clouds ◽  
Camera Systems ◽  
3D Point Clouds ◽  
Dense Matching ◽  
Dense Point ◽  
Processing Techniques ◽  
Object Models ◽  
Survey Planning

3D modeling of cultural monuments is very crucial issue for preparing restoration projects. However, it has challenges such as data acquisition, preparation and processing. 3D modeling of objects can be time consuming and may include some difficulties due to the complexity of the structures. 3D terrestrial laser (TLS) scanning technique is one of the reliable and advantageous methods for 3D reconstruction of monuments. This technique is commonly acknowledged due to its accuracy, speed and flexibility. But the suitability and capability of this technique depends on proper usage, and good survey planning. Magnificent developments in highresolution digital sensor technologies leaded to manufacturing of new camera systems. Parallel to these innovations, development of computer systems and image processing techniques made enable to obtain multiple image-based 3D object models. In the presented study, TLS method has been compared to conventional photogrammetric and image-based dense matching methods. Automatic dense point creation has been realized by our developed algorithm and PIXEL-PHOTO software which generates 3D point clouds from stereo images. The reliability and encountered problems during point cloud measurement process have been discussed. The study area has been chosen as historical Byzantine Land Walls of Istanbul, which constitute a remarkable area defining the ancient city’s historical peninsula.

scholarly journals Photogrammetric Measurement of a Wooden Truss

2018 ◽  
Vol 26 (4) ◽  
pp. 1-10
Author(s):  
Marián Marčiš ◽  
Marek Fraštia
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
3D Modelling ◽  
Complex Objects ◽  
Lighting Conditions ◽  
3D Point Clouds ◽  
Special Cases ◽  
Specific Object ◽  
Photogrammetric Measurement

Abstract Wooden trusses are a very specific object for measurement. They are often very complex and hard to reach; they are characterized by narrow spaces and low-lighting conditions. In recent years, laser scanning technology was mostly used for this task, because of its contactless nature, the possibility of measurement in the dark, and the robustness of the resulting 3D point clouds. Photogrammetry was mostly used in special cases, e.g., for the measurement of a few selected truss components, but not for the 3D modelling of an entire truss. However, the progress in computer vision algorithms is allowing us to accomplish image-based-modelling on very complex objects. The following contribution compares the point clouds of a wooden truss generated by the leading photogrammetry systems with a point cloud from laser scanning. The results confirm the interesting potential of actual photogrammetric methods in the modelling of complex objects such as wooden trusses.

scholarly journals Disturbance Analysis in the Classification of Objects Obtained from Urban LiDAR Point Clouds with Convolutional Neural Networks

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez
Keyword(s):  
Laser Scanning ◽  
Ambient Noise ◽  
Point Clouds ◽  
Density Variation ◽  
Density Reduction ◽  
Manual Intervention ◽  
Point Density ◽  
Disturbance Analysis ◽  
Street Furniture

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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