[Retracted] 3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

Feng, Liying; Weng, Ng Giap; Ma, Liyao

doi:https://doi.org/10.1155/2023/5169781

Wireless Communications and Mobile Computing

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Abstract Introduction Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

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Research Article | Open Access

Volume 2023 | Article ID 5169781 | https://doi.org/10.1155/2023/5169781

[Retracted] 3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

Liying Feng,¹Ng Giap Weng,²and Liyao Ma³

Academic Editor: Hamurabi Gamboa Rosales

Received25 Aug 2022

Revised05 Sept 2022

Accepted13 Sept 2022

Published10 Apr 2023

Abstract

Point cloud data registration is one of the key steps in 3D laser scanning data processing. At present, point cloud data registration has the problems of error and is too much time-consuming. In order to solve the above problems, a 3D point cloud data registration algorithm based on augmented reality technology is proposed, and the 3D data registration model is constructed by combining augmented reality technology, build the evaluation index of 3D point cloud data registration, and carry out the initial registration of 3D point cloud based on the evaluation index. The experiment shows that the 3D point cloud data registration algorithm based on augmented reality technology can more effectively improve the accuracy of data registration and avoid the problem of low efficiency of data registration in practice.

1. Introduction

3D registration technology is the key technology to realize the application of augmented reality [1]. The concept of augmented reality as an extension of augmented reality technology is understood and defined from the perspective of cognitive significance. Taking the research of 3D registration method based on computer vision as the starting point, based on the mathematical theories such as camera model, camera calibration and distortion correction, view geometry and motion inference structure, three registration methods based on augmented reality, and point cloud model and parallel tracking rendering are taken as examples [2]. Two kinds of augmented reality 3D registration methods based on computer vision, model-based, and 3D tracking registration are studied in order to improve the registration accuracy and real-time solve the 3D registration problems in different scene applications. Compared with the concept of augmented reality, this paper studies and defines the concept of augmented reality from the perspective of cognitive science. The basic requirements and key technologies of augmented reality system construction are summarized, and the specific applications of augmented reality are summarized [3].

The Internet of Things (IoT) is a promising platform that unites numerous heterogeneous and ubiquitous items with various connecting and computing capacities with the goal of delivering a picture of the physical world over the network by employing (IoT). The spectrum of devices that can transmit information about the physical world to the Internet is constantly expanding, allowing the Internet of Things to rely on a wide variety of items.

2. 3D Point Cloud Data Registration Algorithm Based on Augmented Reality Technology

2.1. 3D Point Cloud Data Feature Acquisition Model under Augmented Reality Technology

Augmented reality is proposed with the development and progress of computer technology. Human beings themselves are limited to a certain narrow space-time domain, so their ability to understand the objective world is greatly limited. On the other hand, people’s dynamic abilities are extraordinary. With the help of computers and other technologies, human beings have constructed an environment close to reality through augmented reality [4]. In this environment, people can integrate into it, interact with it and even surpass it, and use unlimited imagination, so as to experience the feeling of “transcendence”. As a technology, augmented reality has greatly extended the function of the human brain and become an effective tool to expand human cognitive ability. For the understanding of “augmented reality”, if “virtual” and “reality” are placed at both ends of the balance, then “augmented reality” can be interpreted as “simulation of reality” or “virtual reality”: from the perspective of relationship with reality, augmented reality can be understood as “simulation of reality”. In addition to the imitation of the presence of reality, it focuses on the simulation of the absence of reality. If we understand augmented reality as “virtual reality”, we emphasize showing the absent past or the absent future [5]. Through augmented reality, we can see through the present, reproduce the past, rehearse (present) the future, and simulate the things and phenomena on the smallest or largest space-time scale that people cannot directly perceive or enter in the real environment, which greatly expands the depth and breadth of human cognition. The relationship between virtual, imitation, and simulation is shown in Figure 1.

The significance of VR technology in the cognitive field is understood from the framework of cognitive science: the epistemological view of existence holds that the sense of existence is the relationship established by the cognitive subject processing its input and output information according to the three principles of stability, economy, and predictability. The inverse mirror model of 3D point cloud data under augmented reality technology is shown in Figure 2.

The research of AR system focuses on tracking and registration, virtual real fusion, human-computer interaction, display output, and many other aspects [6]. The system operation process can be divided into four basic steps: obtaining real scene information; Camera pose solution; Generate virtual scene information; and Display after virtual reality fusion. The structure of augmented reality data acquisition is shown in Figure 3.

The goal of augmented reality is to seamlessly combine the input real scene with virtual information, so that users can feel the high integration of virtual and real. In order to achieve this goal, four conditions need to be met: Geometric consistency. The placement of virtual objects in the scene should be consistent, without jitter and drift [7]. The model is real. If the virtual object is a three-dimensional model, it is necessary to ensure the reality of the model shape, texture, and material. Consistent illumination. The lighting conditions of virtual objects should be close to the real environment. Consistent color. If it is a video acquisition input device, it is necessary to consider that the captured image is affected by exposure, noise, and other factors. When drawing a virtual object, it is also necessary to simulate the hardware conditions of acquisition. To meet these four conditions, the construction of augmented reality system needs to use technologies such as display, human-computer interaction, virtual reality fusion, 3D registration, and so on [8]. 3D registration technology is the key technical difficulty of augmented reality application. In this paper, many 3D registration methods involved in the implementation of AR system are summarized and divided into hardware based, computer vision based, and hybrid registration. The induction and classification of AR 3D registration methods are shown in Figure 4.

Based on the analysis of the construction and implementation process of augmented reality system, we can know that the establishment of a relatively perfect augmented reality system generally needs three key technologies: display, tracking registration, and human-computer interaction [9]. Among them, display technology is the foundation, tracking registration technology is the key, and human-computer interaction technology is the expansion requirement of the system. The ultimate goal of augmented reality is to achieve the best fusion of virtual object and real scene. To achieve this goal, or registration process, is the process of placing virtual objects in the correct position in the real environment [10]. A typical augmented reality data processing system is shown in Figure 5.

The process from scene acquisition module to tracking registration module is a key process of augmented reality system [11]. The virtual real registration technology in this process is the focus of this paper. Combining the information of the virtual model and the tracking registration module can form the registration result of the final overlay display [12]. The human-computer interaction module in the figure is not available for all augmented reality systems, but it is the inevitable direction of the development of augmented reality systems in the future [13]. Its main function is to adjust the output of the system according to the wishes of users, so as to make the results of virtual reality registration more practical.

2.2. Registration Evaluation Algorithm of 3D Point Cloud Data

With the development of image acquisition technology, 3D scanning technology is becoming more and more mature. According to the needs of different applications, there are many different technologies to obtain 3D point cloud data. According to different data acquisition methods, it can be roughly divided into two categories: contact method and noncontact method. Among them, the contact method obtains the data information of three-dimensional objects by calculating the depth by actually touching the object surface [14]. The noncontact method refers to projecting additional energy onto the object and calculating the three-dimensional spatial information by the reflection of energy. Recently, the main technology is to calculate the object model data through optics, acoustics, computer vision, and other technologies, so as to obtain its three-dimensional data information. The key of 3D point cloud registration based on geometric invariants is to find the overlapping area between the point cloud views to be matched, and the overlapping area between the views cannot be judged in advance. It is necessary to continuously compare the geometric features of the points on the two point cloud data to be matched, and then select the best matching point pair combination [15]. The point cloud data of 3D geometric model often has the characteristics of massive. The so-called massive means that the number of point clouds of 3D point cloud data is tens of thousands, some even hundreds of thousands. If the geometric characteristics of point cloud data on source point set and target point set are compared one by one, it will consume a lot of time and affect the efficiency of the algorithm. Therefore, before the initial registration of the target point set and the source point set, it is often necessary to extract some representative point cloud data between the two views, and complete the initial splicing based on the selected part of point cloud data. These selected representative point cloud data are called feature points [16]. A spatial grid secondary division strategy is used to search for the nearest point. On the basis of the first uniform division of the grid, considering the number of scattered point sets, the density of scattered point clouds and the size of values, the size of the cube grid is calculated twice, and then the adjacent search of point clouds is completed within the grid, which has high search efficiency. Firstly, read in the point cloud data to be measured, obtain the maximum and minimum values of , , and coordinates of the point set to be measured, calculate the minimum cuboid parallel to the coordinate axis that can contain all point cloud data, and set the length, width, and height of the minimum cuboid bounding box as , and , respectively.

The cuboid bounding box is divided into m_n × n₀ × B small cube grids, where:

where represents the initial estimation of the small cube grid, which is estimated by the empirical formula . in this way, each point cloud data is divided into the cube grid. However, due to the uneven characteristics of scattered point cloud data, the number of point clouds distributed in each cube grid is very uneven, and many empty cubes are generated, resulting in low space utilization [17]. It also affects the efficiency of neighborhood search in subsequent grids. Therefore, it is necessary to divide the point cloud data into quadratic space, traverse, and calculate the number of cube grids containing point cloud data, and set the density of scattered point cloud data as:

The formula represents the number of point clouds per unit volume in a small grid containing point cloud data . Considering the density of point cloud data, the number and value of point cloud data, the secondary space of point cloud data is divided.

Further, focus on the process of rigid body conversion. In order to better the principle of the immediate registration algorithm, we should first understand the characteristics of rigid body motion. In three-dimensional space, objects only move in translation and rotation, which is called rigid body motion. The rigid body motion has important physical significance [18]. It can keep the inner product and measurement unchanged in the process of motion. For the three-dimensional point cloud set, take a point in the set, and the coordinates are expressed as (). After translation, the point is obtained, and its coordinates are expressed as (). The translation process in the space of the three-dimensional point cloud is expressed by the following formula:

where t_x, t_y, t_z, respectively, represent the offset of point on the -axis. The above formula can be equivalent to:

The rotation of 3D point cloud data can be regarded as an extension of 2D space rotation, but it needs to rotate around , and in the process of rotation. The calculation formula of rotation around axis is as follows:

The calculation formula of rotation around axis is as follows:

In three-dimensional space, the process of rotating around any straight line in space can be decomposed into a combination of rotating around and . Let a point () on a line , and the directional cosine of the line is (). Suppose that a point () in dimensional space rotates around the line , and the rotation angle is , set the point coordinates after rotation. Due to the limitation of 3D laser scanning perspective, it is impossible to collect all the point cloud data of the whole scene at one time [19]. In addition, there will be different degrees of influence such as translation and rotation. It is necessary to register some of the collected 3D point cloud data to obtain the whole point cloud data. If you want to get the complete model data in the actual 3D scene, you need to calculate the position relationship between the 3D point clouds from different perspectives, that is, the translation matrix, and then use the obtained translation matrix to transform the coordinates between the 3D point cloud data from different angles, so that all the data are combined into one coordinate system. The whole process is called point cloud registration. Point cloud registration is to calculate the transformation and translation relationship between three-dimensional point clouds by mathematical model, and then align all three-dimensional point clouds from different angles to a unified three-dimensional coordinate system by using the obtained transformation and translation matrix, so that the whole three-dimensional point cloud data model can be obtained. The key to the point cloud registration process is to calculate the coordinate transformation relationship between different 3D point cloud data. Generally, the transformation matrix is represented by , including a 33 rotation matrix and a 31 translation vector . as long as the transformation matrix is multiplied by the 3D source point cloud data, the 3D point cloud data can be aligned under the unified coordinate system [20–21]. Therefore, the complete 3D point cloud can be obtained by gradually transforming each 3D point cloud. The mathematical model of point cloud registration is: suppose two three-dimensional point clouds, which are recorded as three-dimensional source point clouds, respectively and 3D target point cloud the corresponding relationship of at least three noncollinear points is known, and then the rigid rotation matrix and translation variable can be solved by minimizing the following formula according to the corresponding relationship and the least square method:

where is the corresponding matching point of in the 3D source point cloud, and is the number of matching points. After the registration of 3D point cloud data, after meshing, texture mapping, and other operations, we can get the results of 3D reconstruction of the target object. It can be seen that if the result of 3D point cloud registration is poor, it will have an impact on the following steps, resulting in the low quality of the final 3D modeling, and also have a great impact on other 3D applications. It can be seen that 3D point cloud registration is a particularly key part of point cloud data processing. The dynamic registration algorithm is mainly aimed at the sampled data points of motion or deformation [22–24]. The usual method is to fix the three-dimensional laser scanner, and then let the three-dimensional object to be scanned rotate along its own central axis. The parameters required for registration can be obtained directly from the preset rotation speed of three-dimensional objects and the rotation angle between each frame, and the conversion matrix in rigid body transformation can be easily obtained. Because the algorithm adopts rotary scanning, some areas are difficult to scan when scanning 3D target objects, and there are blind areas. It is necessary to scan the blind areas again, and then register with the previously collected data. Finally, the complete point cloud data of 3D model is obtained. The geometric features of point cloud data mainly refer to the points, lines, and surfaces that can reflect the geometric and texture features of the target object. These geometric features are the basis of 3D modeling, run through the whole process of 3D modeling, and affect the accuracy and reliability of each step. Different methods need to be adopted for different target objects. Even the same object needs to extract different features according to different needs [25].

2.3. Implementation of 3D Point Cloud Data Registration

Laser scanning point cloud data is different from general three-dimensional data sets. Point cloud data are scattered, not continuous, and the distribution density is not regular. Therefore, it is difficult to set a set value to divide the space using general rules, otherwise it will cause a large number of pointless space phenomena, or the divided space is not thorough enough, and it is difficult to retrieve spatial data. The conventional grid and octree division are shown in Figure 6.

(a)

(b)

As shown in the figure, the shaded part represents “pointless space”. It can be seen from the figure that the area in the upper left corner is already pointless, but the regular grid still needs to be. Based on the above analysis, each data organization method has its own scope of use and has its own advantages and disadvantages. This paper is based on the point cloud data obtained by laser scanning. Due to the characteristics of point cloud data, the above methods have different disadvantages for the organization and management of point cloud data. This needs to improve or propose new algorithms based on the existing algorithms to find a more suitable index method for point cloud data organization and management. Based on the basic tree structure, this paper studies the advantages of KD tree structure in point cloud data storage and management. The registration of point cloud data sets is called pairwise registration, which is usually obtained by applying an estimated 4 representing translation and rotation ×4 rigid body transformation matrix enables one point cloud data to accurately register with another point cloud data set (target number set). The specific implementation steps are as follows: (1) Key points are extracted from the two data sets according to the same key point selection criteria. (2) Calculate the feature descriptors of all selected key points, respectively. (3) Combine the coordinate positions of the feature descriptors in the two data sets, estimate their corresponding relationship based on the similarity of features and positions between them, and preliminarily estimate the corresponding point pairs. Assuming that the data set is noisy, the wrong corresponding point pairs that affect the registration are removed. Use the remaining correct correspondence to estimate the rigid body transformation and complete the pairwise registration steps in the registration PCL. The registration process of 3D point cloud data is shown in Figure 7.

As shown in the figure, the most important thing in the whole registration process is the extraction of key points and the feature description of key points, so as to ensure the accuracy and efficiency of the corresponding estimation, so as to ensure the correctness of the rigid body transformation matrix estimation in the subsequent process. According to the processes of the two mathematical models listed above, it can be concluded that the two traditional rigid registration models need to obtain the covariance matrix between two three-dimensional point clouds and the centroid of three-dimensional source point set and three-dimensional target point set. At the same time, although the singular value decomposition method is relatively simple, it may fail in the special case of more noise points in three-dimensional data. In general, singular value decomposition method and quaternion method have their own advantages and disadvantages. They are relatively good methods to obtain the results of rigid body rotation matrix and translation matrix. Using the initial registration positions of the two groups of 3D point cloud data obtained in the previous step, continue to iterate, approach the convergence value, obtain the best conversion matrix between 3D point clouds, that is, the registered 3D point cloud data are constructed into triangular patches and meshed [26–27].

3. Analysis of Experimental Results

The experimental system is mainly composed of a computer, a camera, an experimental platform, a skull model, a logo, and a registration support. The image acquisition equipment used in this experiment is a focusing high-definition USB camera with CMOS image sensor, which is convenient to call the camera during the operation of the system. The computer is the core part of the whole virtual real registration system. It is mainly used for three-dimensional registration of models and three-dimensional graphics rendering in the virtual real registration environment. At the same time, it also needs to calculate the registration matrix to realize the real-time operation. Therefore, it is necessary to select a high-performance PC as the hardware development environment of the virtual real registration system. In this paper, thinkpade540 computer is used for the above operations. The hardware configuration environment of the computer is as follows:

CPU: Intel corei54 series.

Memory: 4GB.

Hard disk: 500 g.

Graphics card: NVIDIA geforce840m + intelgmahd4600.

Display: LED backlight 3D display.

The experimental table is a shelf with adjustable height to simulate the surgical environment.

By using vivid 9i to obtain a large number of point cloud data, this paper shows the practical application effect of the algorithm. This paper uses MATLAB to design a simple three-dimensional data registration system. The purpose is to register the two physical point cloud data to be registered based on the accelerated ICP algorithm proposed in this paper, and explain the superior performance of the algorithm in matching accuracy and iteration speed from the aspects of iteration times and error. The practical application effect is consistent with the results of experimental simulation. After a large number of experimental data, as shown in Table 1.

Using statistical analysis method to filter the point cloud data can quickly remove the noise points in the data, and with the increase of point cloud data, the filtering time also increases. When the point cloud data exceeds 1 million, the algorithm takes too long. Other optimization algorithms are used to filter the point cloud data. In order to verify the effectiveness of 3D image intelligent registration method under noise interference based on curvature map, the following experiments were carried out. First, the selected image size is pixels. In order to verify the robustness of this method, 150 experiments are carried out according to the image data, and the comparison results between the three-dimensional image intelligent registration under the traditional method and the three-dimensional image intelligent registration based on curvature map are obtained, as shown in Table 2.

It can be seen from the table that the time of 3D image intelligent registration under the traditional method is longer than that of this method, mainly because when this method registers the image, it uses the depth data, processes the high-frequency region and low-frequency region of 3D discrete group data, and obtains the registration of 3D image through the analysis of characteristic data. The two technologies are used to compare the matching number and matching rate of feature points of panoramic image, and the results are shown in Table 3.

As shown in the above table, the number and matching rate of panoramic features obtained by traditional technology are lower than those obtained by data registration technology. Data registration technology has a high feature point matching rate, which shows that this technology has a high accuracy in describing the features of contour information. In the process of 3D image processing, discrete edge voxels are always closely distributed on both sides of the real continuous hidden boundary surface. Therefore, in the process of processing, we should detect and track the surface containing continuous hidden boundary, and determine the edge voxels in the endpoint set to obtain the boundary surface. The evaluation of image boundary surface information is shown in Table 4.

By comparing the image boundary and surface information, the effect of the three-dimensional image processing platform based on Multimedia Intelligence is more obvious than the traditional three-dimensional image processing method. The system can keep the information of the original image in principle while telling the operation. The experimental results show that the system can obtain and retain image information faster and better while expanding the gray range, successfully achieving the expected effect after three-dimensional image enhancement, and effectively ensure the picture quality after three-dimensional image processing. In order to verify the real-time performance of the registration method, the running time of each module is tested. In the experiment, siftgpu is used to extract SIFT feature points. Since each module runs independently, as long as the computing power is sufficient, the frame rate of the method is determined by the most time-consuming module. The SIFT feature point extraction module takes 45 ms, so the frame rate is about 20fps. The delay of the method is the sum of the running time of each module, that is, about 80 ms. In fact, since the candidate key frames are independent of each other, if intra parallel is adopted for feature matching between the input image and the candidate key frames, the influence of parameter on the delay can be ignored, so the delay can be further reduced. In practical application, users will not notice the delay within 100 ms, so the improvement of registration method based on point cloud model can meet the basic requirements of real-time. The comparison of cloud data registration error values is shown in Figure 8.

Based on the comparative analysis of the above experimental results, compared with the traditional methods, the 3D point cloud data registration method based on augmented reality technology proposed in this paper has significantly lower error value in the process of practical application. The model-based 3D registration method is studied and implemented. Starting from the application requirements of different scenarios, two typical model-based registration methods based on augmented reality and point cloud model are selected for research and improvement, and the performance of the improved method in improving registration accuracy, robustness, and real-time is verified by experiments. The 3D registration method based on augmented reality is relatively simple, with high real-time and accuracy. It is more practical when adding artificial signs to the scene. The effectiveness of the improved method is verified by experimental comparison.

4. Conclusion

The research status of point cloud data registration algorithm is introduced, and the classical algorithms are summarized. The current registration algorithms can be divided into featureless registration algorithm and feature-based registration algorithm. The feature-free registration algorithm does not need to extract features and has high accuracy, while the feature-based registration algorithm needs to spend a lot of time and energy to extract features and solve transformation parameters, and the accuracy is not very high. Therefore, the practicability, accuracy, and reliability of the feature-free registration algorithm are much higher than that of the feature-based registration algorithm. In practical applications, most of them also use featureless registration algorithms. Therefore, the noise and complexity of point cloud registration algorithm is a new research topic for scholars. Based on this, a cloud data registration algorithm based on augmented reality technology is proposed to improve the registration accuracy and efficiency.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

(1) This work was supported by the Hainan Province Philosophy and Social Science Planning Project Research Results, No.: HNSK (YB)20-63; (2) Hainan Provincial Department of Education Science Research Project, No.: HNKY 2021-5; and (3) Hainan Philosophy and Social Science Research Base Project, No.: JD (ZC) 21-53.

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Copyright © 2023 Liying Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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