Improved ICP Algorithm Research Articles

Recognition and position estimation method for stacked untextured parts

To address the challenge of recognizing and estimating the position of untextured stacked parts, which are common in industrial environments, this study proposes an integrated approach that incorporates the YOLOv7 target detection algorithm and point cloud alignment techniques. First, the YOLOv7 algorithm is utilized to quickly identify and locate the 2D position of the part, followed by a mapping technique to transform the 2D region of interest (ROI) into the corresponding 3D point cloud region. In the point cloud processing stage, depth threshold segmentation and Euclidean clustering segmentation methods are used to separate the target part from the background and other interfering objects. The pose estimation stage uses the SAC-IA algorithm for coarse alignment, followed by an improved ICP algorithm that introduces an adaptive weighting mechanism and a global optimization strategy for fine alignment to obtain the final 6D pose of the part. The improved strategy significantly optimizes the point-pair selection and alignment process and enhances the robustness and accuracy of the algorithm. Through experimental validation on publicly available part piece datasets, the results show that the part identification and pose estimation method proposed in this study can realize fast and accurate identification and pose estimation of different shapes, non-textured, and scattered stacked parts, where the position error can reach up to 1mm and the angular error within 1°, which meets the requirements of practical applications.

Three-dimensional modeling of ancient yiyuan architecture using static laser scanning technology

Abstract The awareness of protecting traditional culture and humanistic landscapes is increasing year by year, and the protection of ancient gardens is becoming more and more a consensus among people. This paper takes the architectural profile of Guyi Garden as the research basis, introduces the specific application of 3D laser scanning technology, plans the scope of architectural data acquisition in Guyi Garden, and designs the corresponding architectural data acquisition program. With respect to the acquired point cloud data of GuYiYuan, the bilateral filtering algorithm is used to reduce the noise of the data, the curvature thinning algorithm is combined to reduce the point cloud data of the buildings in GuYiYuan, the point cloud data are spliced based on the connection point, and the ICP algorithm with the introduction of the optimization of the rotational angle constraints is introduced to realize the alignment of the point cloud data. In addition, the shadows generated from the point cloud data are removed by combining the relighting method, and the linear transformation method is also used to globally correct the texture image generated from the point cloud data. The point cloud density was reduced from 0.95m² to 0.01m² when the thinning rate was increased from 8% to 96%, and the alignment error of the improved ICP algorithm under 25dB Gaussian white noise was only 0.083*10-4mm. The maximum error accuracy of the two-dimensional model of Yiyi Nodang generated based on the point cloud data was only 0.007m, and the error of the three-dimensional model was controlled to be within the range of ±9mm. Accurate architectural data can be obtained through the use of 3D laser scanning technology, leading to a new method for digitally preserving historical buildings.

A Framework Study on the Application of AIGC Technology in the Digital Reconstruction of Cultural Heritage

Abstract AIGC is currently a hot field and a future trend in AI applications, and addressing the challenge of digitally reconstructing cultural heritage under the influence of AI technology is a pressing issue that requires immediate resolution. The article proposes an application framework for AIGC technology that is based on refining its meaning and designing a specific process for applying it to the digital reconstruction of cultural heritage. A high-definition camera is used to acquire relevant images of cultural heritage. The image features are extracted by the SIFT algorithm optimized by the PROSAC algorithm. The color features are acquired by combining the color histogram, color moment, and color correlation diagram. The 3D laser scanning technology is used to obtain the 3D point cloud data of the cultural heritage; the KD-tree improved ICP algorithm is introduced to improve the efficiency of point cloud alignment; the dense reconstruction of the 3D point cloud data of the cultural heritage is realized based on CMVS/PMVS; and the immersive 3D experience system of the cultural heritage is constructed by combining with platforms such as Unity3D. The average matching rate of the optimized SITF algorithm to the image features of cultural heritage is about 74.91%, and the maximum alignment time of the ICP algorithm to the cultural heritage point cloud data based on KD-tree is 9.241 s. The cultural heritage immersive 3D experience system has a satisfaction rate of 56.75%, and the density reconstructed model’s surface has an average deviation of only 0.34 mm from the real surface. The user satisfaction rating for the immersive 3D experience system for cultural heritage is 56.75%. Based on AIGC technology, it can revitalize cultural heritage and achieve digital reconstruction and inheritance innovation of cultural heritage.

RESEARCH ON POWER TRANSMISSION CHANNEL CHANGE DETECTION BASED ON MULTI-TEMPORAL POINT CLOUD DATA

Abstract. Airborne LiDAR can directly obtain 3D information of ground objects. By comparing the multi-temporal LiDAR data, ground objects change information of power transmission channel can be detected, providing data support for transmission line operation and maintenance. In this paper, an improved ICP algorithm based on multi-temporal LiDAR point cloud data power transmission channel ground object change detection method is proposed. Firstly, based on the classification of point cloud data, a two-level matching method of multi-temporal point cloud data considering the characteristics of power transmission channel was proposed to achieve accurate registration of point cloud data. Then, change detection and analysis of different types of ground feature point cloud data were carried out through elevation difference. Finally, cluster analysis was carried out on the changed ground feature points to generate multi-temporal relative ratio analysis report. Experimental results show that the proposed method can effectively detect power transmission channel changes.

A method of three-dimensional non-rigid localization of liver tumors based on structured light

In this paper, a new localization method of liver tumors based on structured light is proposed to achieve the accurate resection of liver tumors. Firstly, the preoperative liver model and intraoperative liver surface are completed respectively based on CT sections and structured light. Secondly, the 3D reconstruction of liver surface based on structured light is used as the reference surface for registration. A common ICP algorithm and an improved ICP algorithm based on statistics are proposed to complete the rough and fine registration of liver surface. In this paper, the theory of PBD is introduced, and the registration results are used to guide the preoperative deformation of the liver model. Finally, the method of 3D non-rigid localization of liver tumors proposed in this paper is verified by experiments. In addition, the effectiveness of the proposed method is compared with that of the tumor localization obtained by the finite element analysis and Coherent point drift methods. The experimental results show that the average localization error of liver tumor is 1.24 mm. The time spent during the entire process of tumor localization is kept under fifteen seconds. The method presented in this paper meets the needs of surgeons in the intraoperative process.

A point cloud registration method based on multiple-local-feature matching

A dual-view point cloud registration method has been proposed to enhance the accuracy of point-cloud registration by utilizing multiple local feature descriptors. The method focuses on studying the eigenvalues of the point cloud set at various scales and the impact of descriptors that consist of corresponding curvature radii in reducing misregistration in fast point feature histogram (FPFH). The optimal scale size and number were determined through experimental analysis. SIFT-3D key points of point clouds were extracted to calculate their FPFH and complete initial correspondence matching. Next, a multi-neighborhood local feature descriptor was designed to filter correspondence. A large number of incorrect corresponding point pairs were removed after the initial FPFH matching, and the remaining correct point pairs were used for rough registration. Finally, the improved ICP algorithm was used to reduce registration errors and achieve more accurate dual-view registration. According to the experiments of the public point cloud data set, the dual-view point cloud registration algorithm implemented by the method has good accuracy, high robustness, and more reliable point cloud registration results, which provides a theoretical basis for 3D point-cloud reconstruction.

Point cloud registration method based on Particle Swarm Optimization algorithm and improved ICP

In order to achieve accurate wheel gripping by truss manipulators, accurate point cloud alignment of train wheels is required. In this paper, the wheels are detected by binocular laser displacement sensors. And accurate point cloud alignment is achieved by using the point cloud denoising and streamlining algorithm, particle swarm-based algorithm, and improved ICP algorithm for 3D point cloud data. The experimental results show that reducing noise and data volume can improve the stitching effect and alignment efficiency of 3D point cloud models. The algorithm of this paper is experimentally verified to be 43% better in error accuracy and 93% faster in time-consuming than the traditional ICP algorithm, which verifies the effectiveness of its method.

Transformer Connection Terminal Registration Based on Objective Function

In recent years, the traditional ICP algorithm has high requirements for the initial position of point clouds, and low registration ability for point clouds with low overlap. The Microsoft Kinect depth sensor was used to obtain the point cloud data of the target object from the real scene. Then, pre-processing such as point cloud segmentation, filtering and down sampling. In the coarse registration, the feature point sampling consistency algorithm was used to make the point cloud obtain a better initial position. Finally, a point-to-surface ICP algorithm optimized by linear least squares was proposed in the fine registration. The experimental results show that the root mean square error of the improved algorithm is 0.761mm and the time is 52.32ms. Compared with the ICP algorithm based on SIFT feature points and the improved ICP algorithm based on feature point sampling consistency, the registration accuracy of the improved algorithm is increased by 21.0% and 43.3%, and the speed is increased by 18.9% and 30.2%.

Discontinuity Recognition and Information Extraction of High and Steep Cliff Rock Mass Based on Multi-Source Data Fusion

It is fundamental to acquire accurate point cloud information on rock discontinuities efficiently and comprehensively when evaluating the stability of rock masses. Taking a high and steep cliff as an example, we combined 3D laser scanning and UAV photogrammetry technology to collect rock data, and proposed an intelligent identification method for rock discontinuities based on the multi-source fusion of point clouds. First, the 3D-laser-collected point cloud data were used as the basis to fuse with the UAV-photogrammetry-collected data, and the unified coordinate system and improved ICP algorithm were used to obtain the complete 3D point cloud in the study area. Secondly, we used neighborhood information entropy to achieve adaptive neighborhood-scale selection and to obtain the optimal neighborhood radius for the KNN search, to effectively calculate the point cloud normal vector and rock mass orientation information. Finally, the KDE algorithm and DBSCAN algorithm were combined for rock discontinuity clustering to achieve intelligent identification and information extraction of the rock structural plane. The clustering results were imported into the DSE program developed based on Matlab to calculate the discontinuity spacing and continuity of the rock mass structure, and to efficiently obtain the parameters of rock mass occurrence. The research results showed that this method can effectively solve the problem of incomplete-data-acquisition ground 3D laser scanning in complex geological conditions, and UAV photogrammetry prone to blurred images in depressed areas. When the extraction results were compared with the field-measured rock occurrence, the average dip angle error was about 2°, the average dip direction error was 1°, and the recognition results met the accuracy requirements. The research results provide a feasible scheme for the identification and extraction of discontinuities of high and steep rock masses.

ICP registration with DCA descriptor for 3D point clouds.

Widely used in three-dimensional (3D) modeling, reverse engineering and other fields, point cloud registration aims to find the translation and rotation matrix between two point clouds obtained from different perspectives, and thus correctly match the two point clouds. As the most common point cloud registration method, ICP algorithm, however, requires a good initial value, not too large transformation between the two point clouds, and also not too much occlusion; Otherwise, the iteration would fall into a local minimum. To solve this problem, this paper proposes an ICP registration algorithm based on the local features of point clouds. With this algorithm, a robust and efficient 3D local feature descriptor (density, curvature and normal angle, DCA) is firstly designed by combining the density, curvature, and normal information of the point clouds, then based on the feature description, the correspondence between the point clouds and also the initial registration result are found, and finally, the aforementioned result is used as the initial value of ICP to achieve fine tuning of the registration result. The experimental results on public data sets show that the improved ICP algorithm boosts good registration accuracy and robustness, and a fast running speed as well.

Low-error star pattern recognition based on point cloud matching algorithm*

Aiming at the problem that the matching rate of traditional star map recognition methods is not high enough, this paper proposes a star map recognition method based on the ICP point cloud matching algorithm based on feature point extraction. Feature points are selected through the change of the local normal vector of the point cloud, and then used Common (ICP) algorithm to realize more accurate registration of point clouds. In order to further reduce the error rate of star map matching, an improved ICP algorithm based on feature points is used to accurately register the point cloud. Finally, the matching conditions of several star maps are obtained through MATLAB tool simulation and the matching rate error rate is calculated. Common ICP algorithm The minimum matching error of is 14.224, and the minimum matching error of the improved ICP algorithm based on feature points is 14.0715. Obviously, the star map matching error rate of the ICP algorithm based on feature points is lower, which also verifies the superiority of the improved ICP algorithm.

Weighted Calibration Algorithm of Multiple Laser Scanning Sensors Based on Measurement Error Correction

The calibration of pose transformation relationship among multiple laser scanning sensors or between one sensor and a reference coordinate system is of great importance to the accuracy of 3D laser scanning system. In some applications, such as section inspection of Metro tunnels, the sparsity of measuring points and high demand for accuracy make the calibration process very difficult. This paper proposes an automatically calibration algorithm, named GA-ICP-WHT, based on an improved ICP algorithm with point-wise measurement error corrections. The algorithm consists of stages: coarse registration with GA and fine registration with ICP and weighted Hong-Tan algorithm. Mathematical simulation and application experiments show that the proposed algorithm is accurate and robust for sparse point cloud registration.

A repair strategy based on tool path modification for damaged turbine blade

Turbine blade plays an extremely important role in the aerodynamic performance of aero-engine. And the repair of damaged turbine blades is of great interest for aerospace industries due to continual increase in raw material and manufacturing costs. This manuscript presents a new repair strategy for damaged turbine blades based on tool path modification. The strategy basically involves three crucial procedures: rigid registration of nominal curve and measured points adjacent to damaged region, B-spline approximation of residual errors based on feasible region, and modification of the tool paths used for repairing machining. Rigid registration of nominal curve to measured points is accomplished using improved ICP algorithm adjacent to damaged region. B-spline approximation of residual errors is used to calculate smooth modification amount corresponding to cutter location points. And finally, modified tool paths are used for repair machining, including grinding and polishing. Tip repairing of a rotor turbine blade has been successfully performed to demonstrate the effectiveness of the proposed methodology. The error of repair machined trace between the repaired area and undamaged area is within 0.01 mm which commendably meets the repair machined requirement. And the repaired results have shown that the repairing strategy proposed in this paper is a reliable solution for repairing tip of turbine blades and can guarantee a surface smooth restoration with higher accuracy.

Point Cloud Registration Method for Pipeline Workpieces Based On NDT and Improved ICP Algorithms

A new point cloud registration method based on NDT and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, according to the FPFH algorithm, the feature points of point clouds are extracted to reduce the number of point clouds. Then, the normal distribution transformation (NDT) is used to achieve rough registration of point clouds, so that the two point clouds have relatively good initial position and posture. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the NDT+ICP algorithm are tested respectively, and the results show that the proposed algorithm is faster, with lower error and fewer iterations, which provides a reference for similar problems.

Point Cloud Registration Method for Pipeline Workpieces Based on 4PCS and Improved ICP Algorithms

A new point cloud registration method based on 4PCS and improved ICP algorithm is proposed to solve the problem of point cloud registration of laser scanning workpiece position and pose data on industrial pipeline. Firstly, feature points set of point cloud is extracted from normal vector. Then, the rough registration of point cloud is realized by using the registration algorithm based on feature points (4PCS), and the initial recognition of workpiece position and pose is completed. Finally, based on the traditional ICP algorithm, the k-d tree is used to accelerate the search speed of the corresponding point pairs and complete the accurate registration of the point clouds. The proposed algorithm and the 4PCS+ICP algorithm are tested respectively, and the results show that the algorithm is faster, with lower error and fewer iterations.

Point Cloud Registration Method for Pipeline Workpieces Based on RANSAC and Improved ICP Algorithms

Aiming at the registration problem of laser-scanned workpiece point cloud data, a point cloud registration method based on RANSAC algorithm and improved ICP algorithm is proposed. Firstly, feature points are selected according to the variation law of the normal vector in the original point cloud data, and the initial matching point set is obtained by establishing the histogram (FPFH) of feature points. Then a random sampling consensus (RANSAC) algorithm is applied to the initial data matching. At last, the nearest point iterative algorithm (ICP) accelerated by k-d tree is used for accurate matching, and the quaternion method is used to obtain the registration parameters. The new algorithm and PCA+ICP algorithm are tested and the experimental results are compared. The results show that the new algorithm can achieve registration, and improve the speed and accuracy of registration, which provide a reference for similar problems.

A New Robust and Fast Iterative Closest Point Algorithm for 3D Ship Hull Plate Bending Machines

Liu, X.; Hu, Y., and Xiao, C.-S., 2019. A new robust and fast iterative closest point algorithm for 3D Ship Hull Plate Bending Machines. In: Gong, D.; Zhu, H., and Liu, R. (eds.), Selected Topics in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 94, pp. 264–268. Coconut Creek (Florida), ISSN 0749-0208.The registration accuracy has a direct impact on the processing accuracy of sheet metal in 3D Ship Hull Plate Bending Machines. This study introduces a new distance measure, Root-Mean-Squared iterative closest point (RMS-ICP) algorithm to significantly improve the robustness of the algorithm, and reduce the influence of outliers. At the same time, the 3D point sets registration algorithm is optimized. In addition, the RMS-ICP has been optimized in robustness, registration accuracy and convergence speed in comparison with the original ICP and other improved ICP algorithms. The experiment results support that the performance of the RMS-ICP algorithm is better than other methods. Overall, the RMS-ICP algorithm is very effective in dealing with the point sets registration problem in 3D Ship Hull Plate Bending Machine.

A Hierarchical Skull Point Cloud Registration Method

Skull registration is one of the important steps in craniofacial reconstruction, and its registration accuracy and efficiency have an important impact on the reconstruction results. To solve the problem of low accuracy and efficiency of existing skull registration methods, a hierarchical skull point cloud registration method is proposed in this paper. The whole registration process is divided into a rough registration stage and a fine registration stage. Firstly, feature points are extracted from the pre-processed skull point cloud model, and a local coordinate reference system is established according to the feature points and their neighbor points. The improved spin image is used to construct the local feature descriptor. The feature matching is carried out according to the nearest neighbor algorithm, and the k-means algorithm is used to eliminate the mismatching points to achieve skull rough registration. Then, based on rough registration, we use an improved ICP algorithm to achieve fine registration of the skull. In this process, we use random sampling to reduce the search scale of points and add geometric feature constraints to further eliminate mismatched points. Finally, the whole registration algorithm is applied to the skull point cloud data to verify. The experimental results show that, compared with other methods, the registration effect and efficiency of the proposed method are superior to those of other methods. In order to verify the universality of the method, we also use a common data set for verification. Experiments show that the method is also very effective.

Research and Application of 3D Face Modeling Algorithm Based on ICP Accurate Alignment

This paper focuses on 3D face modeling. First, the panorama is divided into faces. Then the segmented face depth map is filtered first and then registered. Finally, complete the process of modeling the human face. The face segmentation process is mainly to remove the background of the panoramic depth map, and then perform segmentation of the human head. In this paper, select the rough registration method based on SIFT feature point depth map.Use the optimized bidirectional matching algorithm to purify the matched feature point pairs. Then this paper uses the SVD algorithm to find the transformation matrix and complete the depth map rough registration. This paper mainly selects the improved ICP algorithm for accurate registration. The improvement strategy is mainly based on the search acceleration of the K-D tree. The experimental results show that the curvature of the human body changes greatly between the frontal and lateral surfaces of the human body. When the overlapping area is small, the face registration can be performed more accurately and complete face modeling can be performed.

An ICP algorithm based on block path closest point search

At present, the traditional ICP algorithm has the problems of low efficiency and low precision. To solve these two problems, an ICP algorithm based on block path closest point search is proposed in this paper. The idea of the algorithm is as follows: firstly, the point cloud data is divided into blocks, and the nearest point block corresponding to the target point cloud is searched by the path method. Secondly, according to the global method, the nearest point can be determined only by finding the nearest point block, and complete all the closest match. The experimental results show that the improved ICP algorithm has faster speed and higher precision than the traditional ICP algorithm, for a large number of point cloud data advantage is more obvious.