Subway Tunnel Cross-Section Surveying Based on Ground 3D Laser Scanning Data

The 3D laser scanning technology is a hot spot in developed measuring in recent years. In the surface reconstruction of reverse engineering, the 3D laser scanning point cloud data is too large, and is not conducive to the computation, storage and surface reconstruction. After understanding the research status of the point cloud data processing streamline method at home and abroad, and through the analysis of minimum distance algorithm and the angle-chord height combined code method applicable to engineering characteristics, at the same time, the combination algorithm, which is based on the minimum distance algorithm and the angle-chord height combined code method, is proposed to simplify the point cloud data. The scanning point cloud is simplified by using matlab line by line.

Building Information Modeling (BIM) has gained wider acceptance in the Architecture/Engineering/Construction (A/E/C) industries in the U.S. and internationally. Capability of BIM technologies in early detection of spatial conflicts among various building systems and components concurrently designed by different design team members prior to construction has great potential in reducing construction delays, loss of productivity, and overall construction costs. Recent advancements in 3D point cloud technologies, such as 3D laser scanning, can greatly augment BIM technology applications in the A/E/C industry by providing a rapid three-dimensional capture of the built environment in great detail and accuracy. This paper discusses the current approaches of implementing 3D point cloud data in BIM and virtual design and construction (VDC) applications during various stages of a project lifecycle and the challenges associated with processing the huge amount of 3D point cloud data. Conversion from discrete 3D point cloud raster data to geometric/vector BIM data remains to be a labor-intensive process. The needs for intelligent geometric feature detection/reconstruction algorithms for automated point cloud processing and issues related to data management will be discussed. This paper also presents some innovative approaches for integrating 3D point cloud data with BIM to efficiently augment built environment design, construction, and management.

3D laser scanning point cloud data is the basis of free-form surface modeling technique. This paper focuses on the preprocessing of the original point cloud data. Techniques including denoising step using wavelet transform, data reduction and mending using Image ware are discussed in details. Surface reconstruction is finally achieved to demonstrate that the proposed data preprocessing methods improve the easy realization of point cloud data with higher accuracy and faster speed, and thus take a closer step towards the real applications in 3D laser scanning field.

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.

Automatic recognition and classification of vehicles provide a theory and data foundation to solve the road charge, transport safety and vehicle overrun issues, etc., which has become an indispensable part of Intelligent Traffic Management. A vehicle recognition system based on laser scanning point cloud data is designed in this paper. With this system we can accurately acquire 3D point cloud data of vehicles, and preprocess the point cloud original data with the methods including coordinate transformation and median filtering. On the basis of the traditional vehicle features, the variance of vehicle top height is proposed as a feature quantity of vehicle. In addition, we adopts GA-BP neural network as a vehicle type classifier and select appropriate parameters according to the optimal parameters Schaffer recommended such as mutation probability. By analyzing the experimental results, the chromosome fitness function is optimized for the purpose of accelerating the convergence speed of Genetic Algorithms. The result of experiments and its application indicates that these features and the optimized GA-BP neural network selected by this paper have advisable performance on different kinds of vehicle recognition.

In the drifts of underground metal mines, the extraction of rock mass discontinuity characteristics from point cloud models generated with laser scanning has become the main approach. However, the exposure of discontinuities is restricted in drifts, and the size of discontinuities cannot be measured directly. Therefore, it is necessary to use a reasonable sampling tool to estimate the mean trace length of the discontinuities that are mapped in the point cloud model. In this paper, a method to estimate the mean trace length of discontinuities using a three-dimensional (3D) model of a drift (3DM) is proposed. Through the point cloud data of a drift obtained using 3D laser scanning, the information on discontinuities in the surrounding rock was extracted; then, the mean trace length was estimated using 3DEC to set sampling windows on the roof and sidewall in the 3DM. By analyzing the difference between the circular sampling window and the rectangular sampling window using simulated cases, the estimation results showed that the mean trace length obtained using circular measuring windows in the 3DM was closer to the true trace length. Finally, the method was used in a practical engineering case in Jianshan Iron Mine, Panzhihua, Sichuan, China.

Affordable prices for 3D laser range finders and mature software solutions for registering multiple point clouds in a common coordinate system paved the way for new areas of application for 3D point clouds. Nowadays we see 3D laser scanners being used not only by digital surveying experts but also by law enforcement officials, construction workers or archaeologists. Whether the purpose is digitizing factory production lines, preserving historic sites as digital heritage or recording environments for gaming or virtual reality applications -- it is hard to imagine a scenario in which the final point cloud must also contain the points of objects like factory workers, pedestrians, cars or flocks of birds. For most post-processing tasks, moving objects are undesirable not least because moving objects will appear in scans multiple times or are distorted due to their motion relative to the scanner rotation. The main contributions of this work are two postprocessing steps for already registered 3D point clouds. The first method is a new change detection approach based on a voxel grid which allows partitioning the input points into static and dynamic points using explicit change detection and subsequently remove the latter for a point cloud. The second method uses this cleaned point cloud as input for detecting collisions between points of the environment point cloud and a point cloud of a model that is moved through the scene. Our approach on explicit change detection is compared to the state of the art using multiple datasets including the popular KITTI dataset. We show how our solution achieves similar or better F1-scores than an existing solution while at the same time being faster. To detect collisions we do not produce a mesh but approximate the raw point cloud data by spheres or cylindrical volumes. We show how our data structures allow efficient nearest neighbor queries that make our CPU-only approach comparable to a massively-parallel algorithm running on a GPU. The utilized algorithms and data structures are discussed in detail. All our software is freely available for download under the terms of the GNU General Public license. Most of the datasets used in this thesis are freely available as well. We provide shell scripts that allow one to directly reproduce the quantitative results shown in this thesis for easy verification of our findings.

Estimation of plant parameters is prerequisite for fast and precise management of tomato crops on commercial farm level. Destructive estimation methods may be accurate but not suitable when large farm size, spatio-temporal variability or short period of time to obtain information are considered. Therefore, this study was aimed to estimate leaf area (LA) in mature tomato plants from reconstructed geometric plant model based on 3-dimensional (3D) point cloud. A 2D laser scanner was placed on a programable linear conveyor system to move at controlled forward speed. To achieve a high density 3D point cloud of whole plant, close range (< 2 m) scanning was performed at 10 mm/s forward speed. Plant parameters capturing points per plant (PPP), plant height, plant volume, and LA were extracted from the 3D point clouds. Destructive method was utilized in laboratory for reference LA data collection, whereas other required reference data (plant height and canopy area) were measured in-situ. Linear regression models were developed and cross validated to estimate LA using variables from the 3D point clouds and reference LA. Among the models, plant height showed strong correlation with reference LA (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.90, RMSE = 40 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and followed by PPP (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.89, RMSE = 45 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), voxel grid approach (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.56, RMSE = 58 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and slicing approach (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.44, RMSE = 70 cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Results indicate that mobile laser scanning could be a reliable non-destructive LA estimation tool for tomato and may be adapted by the commercial tomato growers for profitable management decisions.

Converting three-dimensional (3D) point cloud data to two-dimensional (2D) image based on virtual structured-light system is a popular method in 3D data compression because the image format is easier to process by the exiting storing and transmitting methods. When this method is used in the 3D point cloud compression, the quantization error is introduced during the coordinate mapping. To solve this problem, a virtual structured-light 3D point cloud compression algorithm based on geometric reshaping is presented. And a least-square system parameter optimization method is proposed to further improve the data decoding accuracy. In the proposed method, the 3D spatial coordinates are reshaped to a 2D matrix first, and then the 2D matrix is stored as a “Holoimage” by using the parameter optimized virtual structured-light system. The quantization error introduced by coordinate mapping between X and Y coordinates of the 3D point cloud and the 2D image pixel coordinates is suppressed, so the decoding accuracy is improved. In addition, the geometric information of the 3D point cloud is hidden synchronously when the 3D point cloud is compressed, which is of great significance in the copyright protection of the point cloud data. Experiments verify the effectiveness of the proposed algorithm. The decoding root mean square error (RMSE) of the proposed method is decreased by 79.86% on average compared with the traditional one under the same compression ratio, and the compression ratio of the proposed method is 1.96 times bigger than the traditional one under the similar decoding accuracy.

The objective of this work is to develop and apply a spectral graph theoretic approach for differentiating between (classifying) additive manufactured (AM) parts contingent on the severity of their dimensional variation from laser-scanned coordinate measurements (3D point cloud). The novelty of the approach is in invoking spectral graph Laplacian eigenvalues as an extracted feature from the laser-scanned 3D point cloud data in conjunction with various machine learning techniques. The outcome is a new method that classifies the dimensional variation of an AM part by sampling less than 5% of the 2 million 3D point cloud data acquired (per part). This is a practically important result, because it reduces the measurement burden for postprocess quality assurance in AM—parts can be laser-scanned and their dimensional variation quickly assessed on the shop floor. To realize the research objective, the procedure is as follows. Test parts are made using the fused filament fabrication (FFF) polymer AM process. The FFF process conditions are varied per a phased design of experiments plan to produce parts with distinctive dimensional variations. Subsequently, each test part is laser scanned and 3D point cloud data are acquired. To classify the dimensional variation among parts, Laplacian eigenvalues are extracted from the 3D point cloud data and used as features within different machine learning approaches. Six machine learning approaches are juxtaposed: sparse representation, k-nearest neighbors, neural network, naïve Bayes, support vector machine, and decision tree. Of these, the sparse representation technique provides the highest classification accuracy (F-score &gt; 97%).

3D point cloud data captured from Oil and Gas (O&amp;G) facilities are becoming increasingly abundant. This data supports the interactive visualization of complex plants, which is central to numerous industrial advancements. As industries seek to innovate and optimize their processes, the versatile use of 3D point cloud data is proving invaluable beyond traditional O&amp;G applications. The precision and comprehensiveness of 3D point cloud data facilitate efficient design, construction, maintenance and operational monitoring across various industrial sectors. Digital Twin (DT) solutions enable this by maintaining 3D representations, such as 3D CAD models, that accurately reflect the current state of facilities. As installations change, the process of 3D scanning is often repeated to capture the latest updates. Engineers and designers then use modelling software to visualize the resulting point cloud and update the facility's 3D representation, incorporating any new modifications. However, this involves interpreting the visual point cloud data and structuring it into meaningful objects (such as valves, tanks, etc.), a process that remains manual, requiring labor-intensive tasks that are prone to errors and incur significant costs due to the need for specialized labor. We propose an Artificial Intelligence approach, that uses a deep learning-based algorithm to automatically segment 3D objects within a point cloud. Our workflow recognizes instances of usual components of production systems, such as control valves, pipes, and transmitters. A state-of-the-art 3D instance segmentation approach is adapted to the O&amp;G domain. Given the lack of publicly available instance segmentation datasets in this domain, we manually annotate 54 scenes from an O&amp;G offshore plant, each of them around 20 m2 in footprint. To further enhance model training, we also incorporated synthetic point clouds. Practical results on real point clouds, acquired from operational facilities and pre-processed as required by commercial DT solutions, indicated a performance of approximately 70% success (f1-score) in segmenting components of O&amp;G production systems, reaching up to 85% for components such as control valves. Accordingly, a 52.4% reduction in costs and efforts associated with manually interpreting 3D point clouds is estimated. Point cloud instance segmentation has attracted much attention in the computer vision research community, and many approaches have been proposed. However, results in the literature are heavily biased toward a few benchmarking datasets, which were acquired in indoor environments like offices and rooms. Few studies have addressed the O&amp;G domain, creating a gap in the literature for achieving more competitive results. This lack of comprehensive research limits the potential for innovation and optimization in the industry. This paper contributes by (i) assessing the performance of state-of-the-art algorithms in the domain of O&amp;G facilities, (ii) proposing modifications to better generalize to components of O&amp;G production systems, (iii) objectively measuring the impact of this algorithm in reducing manual effort for engineers, and (iv) advancing towards a new DT functionality that derives an asset 3D representation, that is faithful to physical reality, through the automatic interpretation of point cloud scans.

Robots typically possess sensors of different modalities, such as colour cameras, inertial measurement units, and 3D laser scanners. Often, solving a particular problem becomes easier when more than one modality is used. However, while there are undeniable benefits to combine sensors of different modalities the process tends to be complicated. Segmenting scenes observed by the robot into a discrete set of classes is a central requirement for autonomy as understanding the scene is the first step to reason about future situations. Scene segmentation is commonly performed using either image data or 3D point cloud data. In computer vision many successful methods for scene segmentation are based on conditional random fields (CRF) where the maximum a posteriori (MAP) solution to the segmentation can be obtained by inference. In this paper we devise a new CRF inference method for scene segmentation that incorporates global constraints, enforcing the sets of nodes are assigned the same class label. To do this efficiently, the CRF is formulated as a relaxed quadratic program whose MAP solution is found using a gradient-based optimisation approach. The proposed method is evaluated on images and 3D point cloud data gathered in urban environments where image data provides the appearance features needed by the CRF, while the 3D point cloud data provides global spatial constraints over sets of nodes. Comparisons with belief propagation, conventional quadratic programming relaxation, and higher order potential CRF show the benefits of the proposed method.

This passage puts forward a kind of simple and convenient three-dimensional laser measurement technology for a small volume and complex shape space data acquisition of the object. This technology mainly uses the camera self calibration method for construction of three-dimensional model algorithm and creates the measurement environment of the measuring technique in machinery and hardware camp. The result of the experiment shows that the desktop 3D laser scanning measuring instrument is convenient, and can acquire spatial location ideal data with low cost.

The boundary of a dangerous rock mass is controlled by the structures of rock masses, and the structural plane information can be obtained through 3D laser point clouds, which provides a data basis for risk assessments. This paper first introduces the principle of terrestrial 3D laser scanning, as well as the identification and interpretation methods of rock mass structural planes. Aiming at a high-steep slope on the left bank of a hydropower station, the location of a unstable rock mass is accurately identified based on 3D point cloud data, and its geometric dimensions and controllable structural plane occurrence are measured. The instability process of the rock mass is simulated using discrete elements, and the results show that the movement has experienced five stages: plane sliding, free falling, collision bouncing, rolling, continuous rebound until static. The maximum speed of the falling rock can reach 84.26 m/s, and the maximum bounce height and horizontal distance are 30m and 40m respectively, posing a great threat to the safety of structures and personnel during construction. This research can provide references to the identification and hazard risk assessment of dangerous rock masses on high-steep slopes.