Extracting sectional contours from scanned point clouds via adaptive surface projection

Form deviation generated during the milling profile process challenges the precision and functionality of industrial fixtures and product manufacturing across various sectors. Inspecting contour profile quality relies on commonly employed contact methods for measuring form deviation. However, the methods employed frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system (RAM-StarliteTM), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The RAM-StarliteTM vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. The results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, and valuable information to improve machining parameters, as well as quality classification.

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.

The density of acquired data by 3D laser scanner is huge and is not convenient for curved face reconstruction, model reconstruction, data storage, data display and transmission.Firstly, the conventional point cloud data classification and simplification method is introduced.Secondly, the angle deviation error and chord deviation error simplification method is researched based on the analysis of characteristic of 3D laser scan point cloud data.Finally, the angle--chord comprehensive simplification method is prompted.The example shows that this method could simplify the scan line point cloud data got by 3D laser scanner directly and effectively.This method could retain the point cloud data which reflects the surface character of object with higher simplification efficiency.

The paper bring forward a new method to monitoring the deformation for the major complicated facility by the 3D laser scanning technology. First, using the 3D Laser Scanning to scan the comparatively complicated industry equipments, then, we get some scan-stations point cloud data about the scanning objects. Utilizing the 3D data processing soft, we can put these point cloud data to a same coordinate to making an integrated point cloud model. Second, using the integrated cloud point model and the 3D reconstruction soft, we can get 3D surface model which has a higher precision. Last, we use the surface model to make the anywhere section plane, which can inspect the sedimentation and deformation degree of the industry equipments according to comparing to the original data. The deformation data is very important. User can use these data to monitor the sedimentation and deformation direction, to service and replace the factory equipments and to guidance the design about the rebuild and extend build. For the precision, we analyze the scanning precision, registration precision and modeling precision to ensuring the user's demand. This method is a new application for the 3D Scanning technology in the industry filed, which can resolve some problems for the big and complex factory equipment in the aspect of monitoring, servicing and rebuild.

In recent years, a 3D laser scanner has increasing attention as one of the modern technologies to digitizing the 3D object surface in manner to obtain a mathematical representation of object's and surface reconstruction. Due to limitations of a 3D laser scanners, the row point cloud, which are acquired from these techniques, included some undesired information such as noise points and associated huge number of points in point cloud. In the present paper, a new proposed point cloud simplification algorithm for scanned object using 3D laser scanner (Matter and Form) has been adopted in manner to extract the necessary geometric features which represented by a 3D surface curvature. This algorithm based on instantaneous calculation of 3D Gaussian curvature for each data point. The curvature value indicates both quality of the topical calculations and a noise isolating and detection in the point cloud set. A MATLAB environment has been adopted to construct a proposed point cloud simplification algorithm program, this program has been proved using a practically case studies. The results proved the effectiveness of the proposed algorithm in local calculation of 3D curvature and detection and isolate the noise points in point cloud where the percent of data which was ignored as noisy data point were (49.29%) and (57.30%), of total data point number after applying the algorithm for the first and second proposed case studies respectively.

Recently, several approaches have been proposed to automatically model indoor environments. Most of such efforts principally rely on the crowd to sense data such as motion traces, images, and WiFi footprints. However, large datasets are usually required to derive precise indoor models which can negatively affect the energy efficiency of the mobile devices participating in the crowd-sensing system. Furthermore, the aforementioned data types are hardly suitable for deriving 3D indoor models. To overcome these challenges, we propose GraMap, a QoS-aware automatic indoor modeling approach through crowd-sensing 3D point clouds. GraMap exploits a recently-developed sensors fusion mechanism, namely Tango technology, to cooperatively collect point clouds from the crowd. Afterward, a set of backend servers extracts the required geometrical information to derive indoor models. For the sake of improving the energy efficiency of the mobile devices, GraMap performs data quality assurance along with 3D data compression. Specifically, we propose a probabilistic quality model---implemented on the mobile devices---to ensure high-quality of the captured point clouds. In this manner, we conserve energy via sidestepping the repetition of sensing queries due to uploading low-quality point clouds. Nevertheless, the resultant indoor models may still suffer from incompleteness and inaccuracies. Therefore, GraMap leverages formal grammars which encode design-time knowledge, i.e. structural information about the building, to enhance the quality of the derived models. To demonstrate the effectiveness of GraMap, we implemented a crowd-sensing Android App to collect point clouds from volunteers. We show that GraMap derives highly-accurate models while reducing the energy costs of pre-processing and reporting the point clouds.

In recent years, three-dimensional (3D) terrestrial laser scanning technologies with higher precision and higher capability are developing rapidly. The growing maturity of laser scanning has gradually approached the required precision as those have been provided by traditional structural monitoring technologies. Together with widely available fast computation for massive point cloud data processing, 3D laser scanning can serve as an efficient structural monitoring alternative for civil engineering communities. Currently most research efforts have focused on integrating/calculating the measured multi-station point cloud data, as well as modeling/establishing the 3D meshes of the scanned objects. Very little attention has been spent on extracting the information related to health conditions and mechanical states of structures. In this study, an automated numerical approach that integrates various existing algorithms for geometric identification and damage detection of structural elements were established. Specifically, adaptive meshes were employed for classifying the point cloud data of the structural elements, and detecting the associated damages from the calculated eigenvalues in each area of the structural element. Furthermore, kd-tree was used to enhance the searching efficiency of plane fitting which were later used for identifying the boundaries of structural elements. The results of geometric identification were compared with M3C2 algorithm provided by CloudCompare, as well as validated by LVDT measurements of full-scale reinforced concrete beams tested in laboratory. It shows that 3D laser scanning, through the established processing approaches of the point cloud data, can offer a rapid, nondestructive, remote, and accurate solution for geometric identification and damage detection of structural elements.

In the process of acquiring point cloud data by a 3D laser scanner, some problems, such as outliers, mixed points, and holes, may be caused in the target point cloud due to the external environment, the discreteness of the laser beam, and the occlusion of objects. In this paper, a point cloud quality optimization and enhancement algorithm is designed. A self‐adaptive octree is established to rasterize the point cloud and calculate the density of each grid, combing with the statistical filtering to remove outliers from the point cloud data. Then, a plane projection method is used for removing the confounding points from the point cloud data. Finally, the point cloud is triangulated and a priority value is set, and then, points are preferentially inserted where the priority value is the largest to repair the holes. Experiments show that while removing outliers and confounding points, the detailed features of the point cloud can be maintained, holes are effectively filled, and the quality of the point cloud is effectively improved.

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.

As buildings have almost come to a saturation point in most developed countries, the management and maintenance of existing buildings have become the major problem of the field. Building Information Modeling (BIM) is the underlying technology to solve this problem. It is a 3D semantic representation of building construction and facilities that contributes to not only the design phase but also the construction and maintenance phases, such as life-cycle management and building energy performance measurement. This study aims at the processes of creating as-built BIM models, which are constructed after the design phase. Point cloud, a set of points in 3D space, is an intermediate product of as-built BIM models that is often acquired by 3D laser scanning and photogrammetry. A raw point cloud typically requires further procedures, e.g. registration, segmentation, classification, etc. In terms of segmentation and classification, machine learning methodologies are trending due to the enhanced speed of computation. However, supervised machine learning methodologies require labelling the training point clouds in advance, which is time-consuming and often leads to inevitable errors. And due to the complexity and uncertainty of real-world environments, the attributes of one point vary from the attributes of others. These situations make it difficult to analyze how one single attribute contributes to the result of segmentation and classification. This study developed a method of producing point clouds from a fast-generating 3D virtual indoor environment using procedural modeling. This research focused on two attributes of simulated point clouds, point density and the level of random errors. According to Silverman (1986), point density is associated with the point features around each output raster cell. The number of points within a neighborhood divided the area of the neighborhood is the point density. However, in this study, there was a little different. The point density was defined as the number of points on a surface divided by the surface area. And the unit is points per square meters (pts/m2). This research compared the performances of a machine learning segmentation and classification algorithm on ten different point cloud datasets. The mean loss and accuracy of segmentation and classification were analyzed and evaluated to show how the point density and level of random errors affect the performance of the segmentation and classification models. Moreover, the real-world point cloud data were used as additional data to evaluate the applicability of produced models.

Statistical process control (SPC) methods have been extensively applied to monitor the quality performance of manufacturing processes to quickly detect and correct out-of-control conditions. As sensor and measurement technologies advance, there is a continual need to adapt and refine SPC methods to effectively and efficiently use these new data-sets. One of the most state-of-the-art dimensional measurement technologies currently being implemented in industry is the 3D laser scanner, which rapidly provides millions of data points to represent an entire manufactured part's surface. Consequently, this data has a great potential to detect unexpected faults, i.e., faults that are not captured by measuring a small number of predefined dimensions. However, in order for this potential to be realized, SPC methods capable of handling these large data-sets need to be developed. This paper presents an approach to performing SPC using point clouds obtained through a 3D laser scanner. The proposed approach transforms high-dimensional point clouds into linear profiles through the use of Q---Q plots, which can be monitored by well established profile monitoring techniques. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios. In addition, experimental studies were performed to determine the effectiveness of the proposed approach using actual point cloud data. The results of these experiments show that the proposed approach can significantly improve the monitoring capabilities for manufacturing parts that are characterized by complex surface geometries.

Automated fibre placement (AFP) machine in-process layup inspection is essential for composite structure fabrication efficiency and quantity improvement. The laser profilometer scanning technique is one of the automated in-process inspection techniques. Laser profilometers can capture a large number of intensive high-resolution point clouds during the layup. To date, few studies have been published about processing point clouds at high speed for in-process layup defect detection and layup feature measurement. In this study, an algorithm called the cross-sectional line-processing algorithm was proposed, and a testbed was constructed to validate the algorithm. The algorithm processes each laser line captured by the laser profilometer and clusters the processed results together. Finally, the defect features and layup features can be segmented and recognized from the collected point cloud. Layup defect types can be recognized, and the dimensions of the layup features can be measured. The experimental results show that this approach can process at most 200 laser lines (about 160 000 points) per second, and the overall defect type recognition accuracy rate reaches about 78%, which meets basic AFP in-process inspection requirements.

The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.

Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

Afterthe construction of subway main structure, in order to realize route adjustmentof alignment and gradient, it needs to survey the cross-section of subwaytunnel. Compared with conventional measuring methods, 3D laser scanning has thecharacteristics of non-contact measurement and can collect space 3D point clouddata with high density, this can improve the working efficiency for the subwaycross-section surveying. Based on the Leica Scanstation 2 scanners this paperanalyzed the 3D laser scanning point cloud data collection procedures and dataprocessing, expounded the subway cross-section surveying method based on pointcloud data; analyzed the feasibility of 3D laser scanning technology in theapplication of tunnel cross-section surveying based on the field validationdata. The results show that the cross-section measured by this method can meetthe technical requirements of route adjustment of alignment and gradient.