3D Digital Modeling of Power Transformers Based on Morphology-Aware Registration Network

This paper reports a novel mutual information (MI) based algorithm for automatic registration of unstructured 3D point clouds comprised of co-registered 3D lidar and camera imagery. The proposed method provides a robust and principled framework for fusing the complementary information obtained from these two different sensing modalities. High-dimensional features are extracted from a training set of textured point clouds (scans) and hierarchical k-means clustering is used to quantize these features into a set of codewords. Using this codebook, any new scan can be represented as a collection of codewords. Under the correct rigid-body transformation aligning two overlapping scans, the MI between the codewords present in the scans is maximized. We apply a James-Stein-type shrinkage estimator to estimate the true MI from the marginal and joint histograms of the codewords extracted from the scans. Experimental results using scans obtained by a vehicle equipped with a 3D laser scanner and an omnidirectional camera are used to validate the robustness of the proposed algorithm over a wide range of initial conditions. We also show that the proposed method works well with 3D data alone.

Three‐dimensional (3D) point cloud registration is a fundamental key issue in 3D reconstruction, 3D object recognition and augmented reality. In this study, the authors propose a novel local feature descriptor called local angle statistics histogram (LASH) for efficient 3D point cloud registration. LASH forms a description of local shape geometries by encoding their properties on angles between the normal vector of the point and the vector formed by the point and other points in its local neighbourhood. In addition, they propose a 3D point cloud registration algorithm based on LASH. The registration algorithm firstly detects triangle matching points with consistent similarity ratios, and then aggregates each pair of triangular matching points into a set of matching points. They can use these matching sets to calculate multiple transformations between two point clouds. Finally, they use the error function to identify the best transformation and to achieve coarse alignment of the two point clouds. Experiments and comparisons with other global algorithms demonstrate that the proposed approach can be applied to register point clouds with considerable or limited overlaps and is robust to noise.

The iterative closest point (ICP) algorithm is widely used in three-dimensional (3D) point cloud registration, and it is very stable and robust. However, its biggest drawback is being easily trapped in a local optimal solution, which results in the incorrect registration result. Currently, there is neither a clear effective range to define whether the ICP algorithm will fall into a local optimum nor a study providing a comprehensive evaluation of the ICP algorithm. In this paper, we take the overlap ratio, angle, distance, and noise as the influencing factors of ICP and evaluate the validity, robustness, accuracy, and efficiency of point-to-point and point-to-plane ICP by using four datasets. We first analyze the effective ranges of the two ICP algorithms with respect to overlap ratio, angle, and distance and then propose a universal effective range for the three factors. Next, the effect of Gaussian noise on the validity and accuracy of two ICPs is evaluated. We also analyze the factors influencing ICP accuracy and explain their changing rules. We finally study the effect of different parameters on the efficiency. All results are compared by using point-to-point and point-to-plane ICP algorithms. The results show that the overlap ratio has no effect on validity, but it has a significant influence on accuracy. The angle has a great impact on the validity and efficiency of ICP but has no effect on accuracy. The distance only affects validity, has a limited effect on efficiency and no effect on accuracy. Meanwhile, Gaussian noise has a little effect on the validity. In addition, the general effective range of point-to-point ICP is larger than that of point-to-plane ICP, but the point-to-plane ICP algorithm presents a better efficiency. The point-to-point ICP is more robust to Gaussian noise with respect to validity, while the point-to-plane is more resistant in terms of accuracy.

Coded light measurement is an important method of non-contact optical measurement. Because of the camera view limitations, it was hard to complete the measurement of the whole surface at a time. Then it needs registration of 3D point clouds. The registration of point clouds can reflect the 3D surface of the object to be measured, which can achieve the full range of requirements. According to the encoded optical 3D measurement system has been built in laboratory, exploring a method which is high accurate and high speed to reach a full range measurement to industrial parts. This paper describes the color target cube. According to the existing method of coordinate transformation (the Nearest Point Iterative Method), Evaluating the registration accuracy. The registration method based on auxiliary three-dimensional targets was been validated, which has its own rationality.

In the process of the construction of China's new power system, we will vigorously promote the research and development of UHV power equipment and the wide application of power electronic devices. UHV power equipment has complex insulation structure and huge volume, bear impact energy load of wind power and photovoltaic of new power system for a long time. It will cost a lot to carry out on-site operation and maintenance tests. Digital twin technology is becoming more and more perfect, and new power system construction is gradually introduced from automobile, aviation and other manufacturing industries. Based on this, this paper introduces the digital twin technology into the high-end power equipment of the new power system, and carries out on-site operation and maintenance simulation test and functional response analysis under high current, high voltage and multi harmonic loads according to its twin model. From the four sensing dimensions of mechanical vibration, gas composition, optical vision and electrical parameters, the improvement of intelligent sensing technology of new power system equipment is analyzed, and the interaction between on-site operating parameters and digital twin model data is realized. On the other hand, GPU computing power expansion technology supporting digital twin multi-source sensing technology is proposed, which can effectively support the dynamic behavior simulation monitoring of equipment from 10-5 seconds to 103 seconds, and the operation life evaluation strategy of high-end equipment is proposed. This paper focuses on the 3D construction of the digital twin model of the high-end equipment of the new power system, and its research method can be extended to the construction of the whole network digital twin model of the new power system. The research results can provide theoretical guidance and technical reference for the application of digital twin technology in high-end power equipment scenarios, and effectively support the safe and stable operation of the new power system with "double high characteristics".

In the control of complex systems, we observe two diametrical trends: model-based control derived from digital twins, and model-free control through AI. There are also attempts to bridge the gap between the two by incorporating learning-based AI algorithms into digital twins to mitigate mismatches between the digital twin model and the physical system. One of the most straightforward approaches to this is direct input adaptation. In this paper, we ask whether it is useful to employ a generic learning algorithm in such a setting, and our conclusion is “not very”. We denote an algorithm to be more useful than another algorithm based on three aspects: 1) it requires fewer data samples to reach a desired minimal performance, 2) it achieves better performance for a reasonable number of data samples, and 3) it accumulates less regret. In our evaluation, we randomly sample problems from an industrially relevant geometry assurance context and measure the aforementioned performance indicators of 16 different algorithms. Our conclusion is that blackbox optimization algorithms, designed to leverage specific properties of the problem, generally perform better than generic learning algorithms, once again finding that “there is no free lunch”. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Digital twins have the potential to improve productivity and quality in complex systems such as manufacturing systems. Their impact on system performance hinges on the accuracy of their digital models around the system’s operating points. Difficult to measure phenomena, such as wear and tear of equipment, however, may cause a mismatch between the digital twin model and the physical system. In this paper, we formalize this problem and compare 16 potential solution strategies under practical aspects. We argue that readily available off-the-shelf blackbox optimization algorithms may prove more useful for this problem, than more recent learning-based approaches. Specifically, gradient-based algorithms will perform best in systems with high-dimensional, continuous, and non-linear performance functions – even in the presence of white measurement noise.

Registration of 3D point clouds is an important task for many different applications today, like reverse engineering, medical imaging, remote sensing, robotics and automation in general.In this paper we explore a new algorithm for rough registration by using the genetic algorithm (GA) which has showed acceptable results in a reasonable amount of computational time.The algorithm is based on the Median Squared Error (MSE) as the fitness function for the GA, and a threshold that defines the maximum distance between corresponding pair of points to be considered inliers.One of the contributions in this paper is a new scheme for adaptable boundary constrains in the algorithm, which makes the global minimum detection faster than the traditional GAs implementations.Another contribution is the separation of the chromosome parameter, i.e. for the first steps of the algorithm we have separated the chromosome encoding into two groups; three genes representing the translation vector (Tx, Ty and Tz) and three genes representing the rotation matrices (Rx, Ry and Rz).Finally, experimental results are presented and discussed using this algorithm.

Recent advance in high-accuracy sensors has made point cloud become the main data format to characterize the three-dimensional world. Since the sensor can only scan and capture the 3D data within a limited field of view, an alignment algorithm is needed to generate the complete 3D scene. Point cloud registration is the solution for alignment problem that aims to estimate the transformation matrix between two frames of different point cloud sets. In this paper, we propose a neural network called OLFF-Net to achieve robust registration of 3D point clouds based on overlapped local feature fusion, which focuses on extracting rotational-invariant local features while providing enough information to achieve accurate alignment. Extensive experiments on representative datasets indicate that the framework can largely outperform competing methods with an average improvement of 16.82% in the metrics over the compared methods. More importantly, it shows significant generalization capability and can be widely applied to point cloud data with multiple complex structures.

Because of its high sensitivity to misalignment, precision grinding of free-form surfaces with micron accuracy requires accurate registration of the surface measurement point cloud. Registration of point clouds obtained with a coordinate measuring machine (CMM) is generally an iterative process of finding optimal coordinate transformation between the CMM frame and the model frame of the workpiece by minimizing the point-to-surface distances with probe radius compensation. For free-form surfaces, frequent calculation of point-to-surface distances consumes very much time, and a trade-off has to be made between the efficiency and the accuracy. This paper presents a method for fast registration of free-form surface point clouds based on the point-to-triangle distance which involves only Delaunay triangulation of a two-dimensional dataset, and the surface normal is quickly calculated from cross product. Probe radius compensation is realized by registering the probe center points with the offset surface. We prove that it is equivalent to registering the probe contact points with the nominal surface through theoretical analysis. The registration problem is then formulated as sequential linear least-square problems with properly defined ball constraints. To validate the method, numerical simulations are presented to show the accuracy of the point-to-triangle distance. The registration algorithm also shows excellent robustness against misalignment of tens of millimeters/degrees. Finally measurement, registration, and grinding of a free-form optical surface are experimentally demonstrated. The surface error obtained after registration is used for compensatory grinding which reduces it to micron level.

Digital twin-based industrial cloud robotics: Framework, control approach and implementation

The 3D laser scanning technology has been widely adopted in the field of workshop automatic assembly. Among them, the point cloud registration is a crucial part of the 3D data processing. Among the 3D data processing steps, point cloud registration is an important one. However, existing 3D point cloud registration methods have issues such as mismatching of feature point pairs, long registration time, and poor registration accuracy. In this paper, an LM-NDT point cloud registration algorithm based on 3D shape context feature (3DSC) is proposed.Firstly, the key points of the 3D point cloud are extracted through the intrinsic shape signature (ISS) to enhance the registration efficiency. Then, the features of the key points are described by the 3D shape context feature. According to the feature points, the error pair is processed using the median distance method, and the initial transformation matrix is calculated through SVD decomposition. Finally, the alignment is accomplished by the LM-NDT algorithm. Compared with the NDT algorithm, the proposed algorithm is more accurate and robust when the initial pose transformation is more significant. The test results demonstrate that the accuracy of the algorithm in the shoe and sole can reach 0.019cm, which remarkably improves the registration efficiency of the traditional SAC-IA and LM-NDT algorithms and has certain engineering application value.

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance.

Abstract. This paper presents a low-cost framework for creating urban digital twins in Virtual Reality (VR) tailored for heritage preservation and smart city applications. The increasing demand for urban digital twins necessitates an integration of diverse data sources to enhance urban management, particularly in historical contexts where traditional methods may lack necessary specificity. To address this, our research integrates advanced 3D survey techniques (mostly low-cost) with publicly available datasets to develop a semantically rich, detailed urban digital model aligned with specific requirements of each unique urban setting.The methodology hinges on three pivotal stages: data acquisition, data management, and data accessibility. Data acquisition involves collecting extensive data both from existing datasets and 3D surveys, emphasizing on identifying optimal, cost-effective solutions suited to the surveyed area. Data management is achieved using a broker database coupled with a Web Application Programming Interface (Web API), ensuring the integrity of original databases while enabling flexible system implementation. Data accessibility extends to a broad range of applications, including GIS, BIM, and customized applications, enhancing the scalability of the digital twin model.The test ground of this system is a VR application developed with Unity, which serves as the interactive platform for the digital twin model. The proposed framework is validated through three case studies in distinct urban settings, each chosen to illustrate the framework's adaptability, versatility, and effectiveness in different urban complexities. The results demonstrate the potential of the digital twin model in facilitating detailed urban management tasks, promoting sustainable heritage conservation, and fostering smarter urban environments.

As an innovative information technology, Digital Twin has greatly promoted the development of intelligent manufacturing in the industry. However, in the field of geotechnical engineering, there are still few researches on this aspect, which is still a “new territory” and “no man's land”. The concept of digital twin coincides with the needs of geotechnical engineering informatization, so the introduction of digital twin technology into the field of geotechnical engineering will help to promote the development process of geotechnical engineering informatization and digitization. This paper puts forward and defines the digital twin model of geotechnical engineering, describes the connotation of the digital twin model, and studies the architecture of the digital twin model of geotechnical engineering. On this basis, the integration and sharing mechanism of geotechnical engineering digital twin data based on BIM technology is proposed. In order to break through the defect that the geotechnical engineering information model has not fully played its role for a long time, an integrated model of geological body and structural body is constructed based on the construction and integration module of geotechnical digital twin model. Furthermore, the geotechnical engineering digital twin simulation analysis module is developed to initially form the geotechnical engineering digital twin model, so as to realize the geotechnical engineering digital design, collaborative construction, visual decision-making and transparent management.

The present work proposes a new method Reg3DFacePtCd for registration of point clouds. The key contribution of the present method is that an unknown face in 3D point cloud form is given to the system and is registered to the already existing known 3D face point clouds using a fast 3D face registration method. The novelty of the present technique is that at first the alignment and registration parameters are found out by initially registering eight key points of the unknown source model to that of the known model. Next, the rest of the point clouds of the unknown model are registered to that of the known model using the same parameters found as above. The main method used for alignment is iterative closest point (ICP) using point-based technique followed by registration in the least squares sense. Mainly there are two significant contributions. Firstly, we have developed a new mathematical model facial landmark point based model across poses to obtain the target or the known model to which all the unknown models will be registered. Secondly, a novel way to accelerate point cloud matching by reducing the number of points has been proposed. Using a small number of points necessarily would speed up the registration process but may inculcate errors. So, to determine the registration quality of the fundamental eight key points on which the entire registration process is based, a new robust metric namely ICV (ICP certainty vector) consisting of several key components have been used. Finally, we have addressed several important face registration issues like pre-processing, convergence and quality of registration of the entire facial point cloud based on the eight key points. Extensive experimentation on Frav3D, GavabDB, and the Bosphorus databases on a high-performance computing environment show the novelty and robustness of the method.