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Choi, Ouk, Min-Gyu Park, and Youngbae Hwang. "Iterative K-Closest Point Algorithms for Colored Point Cloud Registration." Sensors 20, no. 18 (September 17, 2020): 5331. http://dx.doi.org/10.3390/s20185331.
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We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.
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Liu, Huikai, Yue Zhang, Linjian Lei, Hui Xie, Yan Li, and Shengli Sun. "Hierarchical Optimization of 3D Point Cloud Registration." Sensors 20, no. 23 (December 7, 2020): 6999. http://dx.doi.org/10.3390/s20236999.
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Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.
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Feng, Youyang, Qing Wang, and Hao Zhang. "Total Least-Squares Iterative Closest Point Algorithm Based on Lie Algebra." Applied Sciences 9, no. 24 (December 7, 2019): 5352. http://dx.doi.org/10.3390/app9245352.
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In geodetic surveying, input data from two coordinates are needed to compute rigid transformations. A common solution is a least-squares algorithm based on a Gauss–Markov model, called iterative closest point (ICP). However, the error in the ICP algorithm only exists in target coordinates, and the algorithm does not consider the source model’s error. A total least-squares (TLS) algorithm based on an errors-in-variables (EIV) model is proposed to solve this problem. Previous total least-squares ICP algorithms used a Euler angle parameterization method, which is easily affected by a gimbal lock problem. Lie algebra is more suitable than the Euler angle for interpolation during an iterative optimization process. In this paper, Lie algebra is used to parameterize the rotation matrix, and we re-derive the TLS algorithm based on a GHM (Gauss–Helmert model) using Lie algebra. We present two TLS-ICP models based on Lie algebra. Our method is more robust than previous TLS algorithms, and it suits all kinds of transformation matrices.
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Sun, Jin, Zedong Huang, Xinglong Zhu, Li Zeng, Yuan Liu, and Jing Ding. "Deformation corrected workflow for maxillofacial prosthesis modelling." Advances in Mechanical Engineering 9, no. 2 (February 2017): 168781401769228. http://dx.doi.org/10.1177/1687814017692286.
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The purpose of this article is to describe a deformation corrected workflow for maxillofacial prosthesis modelling based on the improved Laplace and iterative closest point–based iterative algorithms. For incomplete maxillofacial data with local deformed symmetrical features, the Laplace algorithm with rotation invariants was demonstrated that the operations can recover the local deformation while preserving the surface geometric detail; the M-estimation iterative closest point–based iterative algorithm integrated with the extended Gaussian image ensures the precision of the symmetry plane, making the outer point having almost no effect on the minimum process. The additional experiments also verified the ability of deformation corrected maxillofacial prosthesis modelling. Case study confirmed that this workflow is attractive and has potential to design the desired maxillofacial prosthesis for correcting the deformed oral soft tissue. The results of this study improve the quality of maxillofacial prostheses modelling. This technique will facilitate modelling of maxillofacial prostheses while helping the patients predict the effect before the prosthesis is manufactured. In addition, this deformation corrected workflow has great potential for improving the development of maxillofacial prosthesis modelling software.
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Wujanz, Daniel, Michael Avian, Daniel Krueger, and Frank Neitzel. "Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring." Earth Surface Dynamics 6, no. 2 (April 16, 2018): 303–17. http://dx.doi.org/10.5194/esurf-6-303-2018.
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Abstract. Current research questions in the field of geomorphology focus on the impact of climate change on several processes subsequently causing natural hazards. Geodetic deformation measurements are a suitable tool to document such geomorphic mechanisms, e.g. by capturing a region of interest with terrestrial laser scanners which results in a so-called 3-D point cloud. The main problem in deformation monitoring is the transformation of 3-D point clouds captured at different points in time (epochs) into a stable reference coordinate system. In this contribution, a surface-based registration methodology is applied, termed the iterative closest proximity algorithm (ICProx), that solely uses point cloud data as input, similar to the iterative closest point algorithm (ICP). The aim of this study is to automatically classify deformations that occurred at a rock glacier and an ice glacier, as well as in a rockfall area. For every case study, two epochs were processed, while the datasets notably differ in terms of geometric characteristics, distribution and magnitude of deformation. In summary, the ICProx algorithm's classification accuracy is 70 % on average in comparison to reference data.
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Cutter, Jennifer R., Iain B. Styles, Aleš Leonardis, and Hamid Dehghani. "Image-based Registration for a Neurosurgical Robot: Comparison Using Iterative Closest Point and Coherent Point Drift Algorithms." Procedia Computer Science 90 (2016): 28–34. http://dx.doi.org/10.1016/j.procs.2016.07.006.
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Wu, Lu-shen, Guo-lin Wang, and Yun Hu. "Iterative closest point registration for fast point feature histogram features of a volume density optimization algorithm." Measurement and Control 53, no. 1-2 (January 2020): 29–39. http://dx.doi.org/10.1177/0020294019878869.
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Motivated by the high speed but insufficient precision of the existing fast point feature histogram algorithm, a new fast point feature histogram registration algorithm based on density optimization is proposed. In this method, a 44-section blank feature histogram is first established, and then a principal component analysis is implemented to calculate the normal of each point in the point cloud. By translating the coordinate system in the established local coordinate system, the normal angle of each point pair and its weighted neighborhood are obtained, and then a fast point feature histogram with 33 sections is established. The reciprocal of the volume density for the central point and its weighted neighborhood are calculated simultaneously. The whole reciprocal space is divided into 11 sections. Thus, a density fast point feature histogram with 44 sections is obtained. On inputting the testing models, the initial pose of the point cloud is adjusted using the traditional fast point feature histogram and the proposed algorithms, respectively. Then, the iterative closest point algorithm is incorporated to complete the fine registration test. Compared with the traditional fine registration test algorithm, the proposed optimization algorithm can obtain 44 feature parameters under the condition of a constant time complexity. Moreover, the proposed optimization algorithm can reduce the standard deviation by 8.6% after registration. This demonstrates that the proposed method encapsulates abundant information and can achieve a high registration accuracy.
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Martínez, Jorge L., Javier González, Jesús Morales, Anthony Mandow, and Alfonso J. García-Cerezo. "Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms." Journal of Field Robotics 23, no. 1 (January 2006): 21–34. http://dx.doi.org/10.1002/rob.20104.
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Bedkowski, Janusz, Timo Röhling, Frank Hoeller, Dirk Shulz, and Frank E. Schneider. "Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems." Foundations of Computing and Decision Sciences 42, no. 3 (September 1, 2017): 275–95. http://dx.doi.org/10.1515/fcds-2017-0014.
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AbstractThis work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.
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Ning, Zhixiong, Xin Wang, Jun Wang, and Huafeng Wen. "Vehicle pose estimation algorithm for parking automated guided vehicle." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989133. http://dx.doi.org/10.1177/1729881419891335.
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Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.
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Wu, Ming-Long, Jong-Chih Chien, Chieh-Tsai Wu, and Jiann-Der Lee. "An Augmented Reality System Using Improved-Iterative Closest Point Algorithm for On-Patient Medical Image Visualization." Sensors 18, no. 8 (August 1, 2018): 2505. http://dx.doi.org/10.3390/s18082505.
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In many surgery assistance systems, cumbersome equipment or complicated algorithms are often introduced to build the whole system. To build a system without cumbersome equipment or complicated algorithms, and to provide physicians the ability to observe the location of the lesion in the course of surgery, an augmented reality approach using an improved alignment method to image-guided surgery (IGS) is proposed. The system uses RGB-Depth sensor in conjunction with the Point Cloud Library (PCL) to build and establish the patient’s head surface information, and, through the use of the improved alignment algorithm proposed in this study, the preoperative medical imaging information obtained can be placed in the same world-coordinates system as the patient’s head surface information. The traditional alignment method, Iterative Closest Point (ICP), has the disadvantage that an ill-chosen starting position will result only in a locally optimal solution. The proposed improved para-alignment algorithm, named improved-ICP (I-ICP), uses a stochastic perturbation technique to escape from locally optimal solutions and reach the globally optimal solution. After the alignment, the results will be merged and displayed using Microsoft’s HoloLens Head-Mounted Display (HMD), and allows the surgeon to view the patient’s head at the same time as the patient’s medical images. In this study, experiments were performed using spatial reference points with known positions. The experimental results show that the proposed improved alignment algorithm has errors bounded within 3 mm, which is highly accurate.
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Xu, Guangxuan, Yajun Pang, Zhenxu Bai, Yulei Wang, and Zhiwei Lu. "A Fast Point Clouds Registration Algorithm for Laser Scanners." Applied Sciences 11, no. 8 (April 12, 2021): 3426. http://dx.doi.org/10.3390/app11083426.
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Point clouds registration is an important step for laser scanner data processing, and there have been numerous methods. However, the existing methods often suffer from low accuracy and low speed when registering large point clouds. To meet this challenge, an improved iterative closest point (ICP) algorithm combining random sample consensus (RANSAC) algorithm, intrinsic shape signatures (ISS), and 3D shape context (3DSC) is proposed. The proposed method firstly uses voxel grid filter for down-sampling. Next, the feature points are extracted by the ISS algorithm and described by the 3DSC. Afterwards, the ISS-3DSC features are used for rough registration with the RANSAC algorithm. Finally, the ICP algorithm is used for accurate registration. The experimental results show that the proposed algorithm has faster registration speed than the compared algorithms, while maintaining high registration accuracy.
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Wan, Yan, Jun Lu, and Ao Qiong Li. "Registration of 3D Point Cloud of Human Body Based on the Range Images and RGB Images." Applied Mechanics and Materials 738-739 (March 2015): 656–61. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.656.
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The existing 3D reconstruction techniques rarely can be easily used in people's daily life, and the traditional registration algorithms have the drawback of massive calculation. In this paper it presented a registration algorithm of body point cloud based on RGB images and Range images. First, it used kinect to obtain the RGB images and Range images from different perspectives. Then it extracted the pairs of 2D feature points on RGB images using scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithm application for the coarse registration and used the improved iterative closest point (ICP) algorithm for the fine registration. Second, it eliminated the background information and the noise points of the model edges. Finally it completed the registration process. Experimental results show that the algorithm can accurately accomplish the body point clouds registration using the low-cost instrument and has a relatively high efficiency.
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Dantanarayana, Harshana G., and Jonathan M. Huntley. "Object recognition and localization from 3D point clouds by maximum-likelihood estimation." Royal Society Open Science 4, no. 8 (August 2017): 160693. http://dx.doi.org/10.1098/rsos.160693.
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We present an algorithm based on maximum-likelihood analysis for the automated recognition of objects, and estimation of their pose, from 3D point clouds. Surfaces segmented from depth images are used as the features, unlike ‘interest point’-based algorithms which normally discard such data. Compared to the 6D Hough transform, it has negligible memory requirements, and is computationally efficient compared to iterative closest point algorithms. The same method is applicable to both the initial recognition/pose estimation problem as well as subsequent pose refinement through appropriate choice of the dispersion of the probability density functions. This single unified approach therefore avoids the usual requirement for different algorithms for these two tasks. In addition to the theoretical description, a simple 2 degrees of freedom (d.f.) example is given, followed by a full 6 d.f. analysis of 3D point cloud data from a cluttered scene acquired by a projected fringe-based scanner, which demonstrated an RMS alignment error as low as 0.3 mm.
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Krell, Gerald, Nazila Saeid Nezhad, Mathias Walke, Ayoub Al-Hamadi, and Günther Gademann. "Assessment of Iterative Closest Point Registration Accuracy for Different Phantom Surfaces Captured by an Optical 3D Sensor in Radiotherapy." Computational and Mathematical Methods in Medicine 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/2938504.
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An optical 3D sensor provides an additional tool for verification of correct patient settlement on a Tomotherapy treatment machine. The patient’s position in the actual treatment is compared with the intended position defined in treatment planning. A commercially available optical 3D sensor measures parts of the body surface and estimates the deviation from the desired position without markers. The registration precision of the in-built algorithm and of selected ICP (iterative closest point) algorithms is investigated on surface data of specially designed phantoms captured by the optical 3D sensor for predefined shifts of the treatment table. A rigid body transform is compared with the actual displacement to check registration reliability for predefined limits. The curvature type of investigated phantom bodies has a strong influence on registration result which is more critical for surfaces of low curvature. We investigated the registration accuracy of the optical 3D sensor for the chosen phantoms and compared the results with selected unconstrained ICP algorithms. Safe registration within the clinical limits is only possible for uniquely shaped surface regions, but error metrics based on surface normals improve translational registration. Large registration errors clearly hint at setup deviations, whereas small values do not guarantee correct positioning.
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Sobreira, Héber, Carlos M. Costa, Ivo Sousa, Luis Rocha, José Lima, P. C. M. A. Farias, Paulo Costa, and A. Paulo Moreira. "Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform." Journal of Intelligent & Robotic Systems 93, no. 3-4 (January 23, 2018): 533–46. http://dx.doi.org/10.1007/s10846-017-0765-5.
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Juang, Jih Gau, and Jia An Wang. "Indoor Map Building by Laser Sensor and Positioning Algorithms." Applied Mechanics and Materials 764-765 (May 2015): 752–56. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.752.
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This study uses a wheeled mobile robot (WMR) to explore unknown indoor environment and build up a map of the unknown environment. The robot utilizes laser measurement sensor with a indoor localization system to detect obstacles and identify unknown environment. The localization system provides the position of the robot and is used for map comparison. Fuzzy theory is applied to controller design. The proposed control scheme can control the wheeled mobile robot move along walls and avoid obstacles. The Iterative Closest Point (ICP) and the KD-tree are utilized. With sensed data of obstructions and walls, a map of unknown environment can be generated by curve fitting methods.
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Мирненко, М. Д., Д. М. Крицький, О. К. Погудіна, and О. С. Крицька. "ПОРІВНЯЛЬНИЙ АНАЛІЗ МЕТОДІВ ПОШУКУ НАЙБЛИЖЧИХ ТОЧОК НА ЗОБРАЖЕННЯХ ОБ’ЄКТІВ ТЕХНІЧНИХ СИСТЕМ." Open Information and Computer Integrated Technologies, no. 92 (September 6, 2021): 123–30. http://dx.doi.org/10.32620/oikit.2021.92.11.
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The subject of the study is the process of mapping the construction of point clouds of technical systems using the algorithm of the nearest points. The goal is to minimize the alignment criterion by converting a set of cloud points Y into a set of cloud points X in a software product that uses an iterative closest point (ICP) algorithm. Objectives: to analyze the properties of input images that contain point clouds; to review the algorithms for identifying and comparing key points; implement a cloud comparison algorithm using the ISR algorithm; consider an example of the algorithm for estimating the approximate values of the elements of mutual orientation; implement software that allows you to compare files that contain point clouds and draw conclusions about the possibility of comparing them. The methods used are: search for points using the algorithm of iterative nearest points, the algorithm for estimating the approximate values of the elements of mutual orientation, the method of algorithm theory for the analysis of file structures STL (standard template library - format template library) format. The following results were obtained. The choice of the ICP algorithm for the task of reconstruction of the object of technical systems is substantiated; the main features of the ISR algorithm are considered; the algorithm of comparison of key points, and also optimization that allows reducing criterion of combination at the reconstruction of three-dimensional objects of technical systems results. Conclusions. The study found that the iterative near-point algorithm is more detailed and accurate when modeling objects. At the same time, this method requires very accurate values and when calculating the degree of proximity, the complexity of calculation by this algorithm increases many times. Whereas the algorithm for estimating the approximate values of the elements of mutual orientation does not require information about the approximate orientation of the point clouds, which simplifies the work and reduces the simulation time. It was found that not all files are comparable. Therefore, the software is implemented, which gives an opinion on the possibility of comparing points in the proposed two files, which contain clouds of points in the structure of the STL format.
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Zhang, Wentao, Guodong Zhai, Zhongwen Yue, Tao Pan, and Ran Cheng. "Research on Visual Positioning of a Roadheader and Construction of an Environment Map." Applied Sciences 11, no. 11 (May 28, 2021): 4968. http://dx.doi.org/10.3390/app11114968.
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The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and build a navigation map of a roadway is presented. In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. An iterative equation based on Levenberg–Marquadt is derived, which can achieve the optimal estimation of the body pose. In terms of mapping, LiDAR is used to experimentally construct the grid map based on open-source algorithms, such as Gmapping, Cartographer, Karto, and Hector. A point cloud map, octree map, and compound map are experimentally constructed based on the open-source library RTAB-MAP. By setting parameters, such as the expansion radius of an obstacle and the updating frequency of the map, a cost map for the navigation of a roadheader is established. Combined with algorithms, such as Dijskra and timed-elastic-band, simulation experiments show that the combination of octree map and cost map can support global path planning and local obstacle avoidance.
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Yin, Chao, Haoran Li, Zhinan Hu, and Ying Li. "Application of the Terrestrial Laser Scanning in Slope Deformation Monitoring: Taking a Highway Slope as an Example." Applied Sciences 10, no. 8 (April 18, 2020): 2808. http://dx.doi.org/10.3390/app10082808.
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Slope deformation monitoring is the prerequisite for disaster risk assessment and engineering control. Terrestrial laser scanning (TLS) is highly applicable to this field. Coarse registration method of point cloud based on scale-invariant feature transform (SIFT) feature points and fine registration method based on the k-dimensional tree (K-D tree) improved iterative closest point (ICP) algorithm were proposed. The results show that they were superior to other algorithms (such as speeded-up robust features (SURF) feature points, Harris feature points, and Levenberg-Marquardt (LM) improved ICP algorithm) when taking the Stanford Bunny as an example, and had high applicability in coarse and fine registration. In order to integrate the advantages of point measurement and surface measurement, an improved point cloud comparison method was proposed and the optimal model parameters were determined through model tests. A case study was conducted on the left side of the K146 + 150 point at S236 Boshan section, Shandong Province, and research results show that from 14 August 2018 and 9 November 2019, the overall deformation of the slope was small with a maximum value of 0.183 m, and the slope will continue to maintain a stable state without special inducing factors such as earthquake, heavy rainfall and artificial excavation.
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Kangal, Fatih, and Emre Mengi. "Nonsmooth algorithms for minimizing the largest eigenvalue with applications to inner numerical radius." IMA Journal of Numerical Analysis 40, no. 4 (November 13, 2019): 2342–76. http://dx.doi.org/10.1093/imanum/drz041.
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Abstract Nonsmoothness at optimal points is a common phenomenon in many eigenvalue optimization problems. We consider two recent algorithms to minimize the largest eigenvalue of a Hermitian matrix dependent on one parameter, both proven to be globally convergent unaffected by nonsmoothness. One of these algorithms models the eigenvalue function with a piece-wise quadratic function and is effective in dealing with nonconvex problems. The other algorithm projects the Hermitian matrix into subspaces formed of eigenvectors and is effective in dealing with large-scale problems. We generalize the latter slightly to cope with nonsmoothness. For both algorithms we analyze the rate of convergence in the nonsmooth setting, when the largest eigenvalue is multiple at the minimizer and zero is strictly in the interior of the generalized Clarke derivative, and prove that both algorithms converge rapidly. The algorithms are applied to, and the deduced results are illustrated on the computation of the inner numerical radius, the modulus of the point on the boundary of the field of values closest to the origin, which carries significance for instance for the numerical solution of a symmetric definite generalized eigenvalue problem and the iterative solution of a saddle point linear system.
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Shu, Zichao, Songxiao Cao, Qing Jiang, Zhipeng Xu, Jianbin Tang, and Qiaojun Zhou. "Pairwise Registration Algorithm for Large-Scale Planar Point Cloud Used in Flatness Measurement." Sensors 21, no. 14 (July 16, 2021): 4860. http://dx.doi.org/10.3390/s21144860.
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In this paper, an optimized three-dimensional (3D) pairwise point cloud registration algorithm is proposed, which is used for flatness measurement based on a laser profilometer. The objective is to achieve a fast and accurate six-degrees-of-freedom (6-DoF) pose estimation of a large-scale planar point cloud to ensure that the flatness measurement is precise. To that end, the proposed algorithm extracts the boundary of the point cloud to obtain more effective feature descriptors of the keypoints. Then, it eliminates the invalid keypoints by neighborhood evaluation to obtain the initial matching point pairs. Thereafter, clustering combined with the geometric consistency constraints of correspondences is conducted to realize coarse registration. Finally, the iterative closest point (ICP) algorithm is used to complete fine registration based on the boundary point cloud. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of boundary extraction and registration performance.
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Wujanz, D., L. Barazzetti, M. Previtali, and M. Scaioni. "A COMPARATIVE STUDY AMONG THREE REGISTRATION ALGORITHMS: PERFORMANCE, QUALITY ASSURANCE AND ACCURACY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 779–86. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-779-2019.
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<p><strong>Abstract.</strong> A critical task in every terrestrial laser scanning project is the transformation (addressed to as registration or alignment) of multiple point clouds into a common reference system. Even though this operation appears to be a solved and well-understood problem, the vast majority of available techniques still lack meaningful quality measures that allow the user to understand and analyze the final outputs. The erroneous estimation of registration parameters may cause systematic biases that falsify those subsequently outcomes such as deformation measurements on historical buildings, CAD-drawings of individual elements, or 3D models devoted to analyze the verticality of a tower. Thus, this article compares three common registration algorithms, namely target-based registration, the Iterative-Closest Point algorithm (ICP) as well as a plane-based approach on examples related to different case studies concerning historical buildings.</p>
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Lo, Frank, Yingnan Sun, Jianing Qiu, and Benny Lo. "Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map." Nutrients 10, no. 12 (December 18, 2018): 2005. http://dx.doi.org/10.3390/nu10122005.
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An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items.
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Sheng, Buyun, Feiyu Zhao, Xiyan Yin, Chenglei Zhang, Hui Wang, and Peide Huang. "A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing." Mathematical Problems in Engineering 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/4673849.
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The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.
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Zheng, Li, and Zhukun Li. "Virtual Namesake Point Multi-Source Point Cloud Data Fusion Based on FPFH Feature Difference." Sensors 21, no. 16 (August 12, 2021): 5441. http://dx.doi.org/10.3390/s21165441.
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There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terrestrial laser scanning, etc. Different sensors use different processing methods. They have their own advantages and disadvantages in terms of accuracy, range and point cloud magnitude. Point cloud fusion can combine the advantages of each point cloud to generate a point cloud with higher accuracy. Following the classic Iterative Closest Point (ICP), a virtual namesake point multi-source point cloud data fusion based on Fast Point Feature Histograms (FPFH) feature difference is proposed. For the multi-source point cloud with noise, different sampling resolution and local distortion, it can acquire better registration effect and improve the accuracy of low precision point cloud. To find the corresponding point pairs in the ICP algorithm, we use the FPFH feature difference, which can combine surrounding neighborhood information and have strong robustness to noise, to generate virtual points with the same name to obtain corresponding point pairs for registration. Specifically, through the establishment of voxels, according to the F2 distance of the FPFH of the target point cloud and the source point cloud, the convolutional neural network is used to output a virtual and more realistic and theoretical corresponding point to achieve multi-source Point cloud data registration. Compared with the ICP algorithm for finding corresponding points in existing points, this method is more reasonable and more accurate, and can accurately correct low-precision point cloud in detail. The experimental results show that the accuracy of our method and the best algorithm is equivalent under the clean point cloud and point cloud of different resolutions. In the case of noise and distortion in the point cloud, our method is better than other algorithms. For low-precision point cloud, it can better match the target point cloud in detail, with better stability and robustness.
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Rajendra, Y. D., S. C. Mehrotra, K. V. Kale, R. R. Manza, R. K. Dhumal, A. D. Nagne, and A. D. Vibhute. "Evaluation of Partially Overlapping 3D Point Cloud's Registration by using ICP variant and CloudCompare." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 891–97. http://dx.doi.org/10.5194/isprsarchives-xl-8-891-2014.
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Terrestrial Laser Scanners (TLS) are used to get dense point samples of large object’s surface. TLS is new and efficient method to digitize large object or scene. The collected point samples come into different formats and coordinates. Different scans are required to scan large object such as heritage site. Point cloud registration is considered as important task to bring different scans into whole 3D model in one coordinate system. Point clouds can be registered by using one of the three ways or combination of them, Target based, feature extraction, point cloud based. For the present study we have gone through Point Cloud Based registration approach. We have collected partially overlapped 3D Point Cloud data of Department of Computer Science & IT (DCSIT) building located in Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. To get the complete point cloud information of the building we have taken 12 scans, 4 scans for exterior and 8 scans for interior façade data collection. There are various algorithms available in literature, but Iterative Closest Point (ICP) is most dominant algorithms. The various researchers have developed variants of ICP for better registration process. The ICP point cloud registration algorithm is based on the search of pairs of nearest points in a two adjacent scans and calculates the transformation parameters between them, it provides advantage that no artificial target is required for registration process. We studied and implemented three variants Brute Force, KDTree, Partial Matching of ICP algorithm in MATLAB. The result shows that the implemented version of ICP algorithm with its variants gives better result with speed and accuracy of registration as compared with CloudCompare Open Source software.
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Gu, Bo, Jianxun Liu, Huiyuan Xiong, Tongtong Li, and Yuelong Pan. "ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature." Sensors 21, no. 10 (May 17, 2021): 3489. http://dx.doi.org/10.3390/s21103489.
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In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.
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Zhuangsheng, Zhu, Guo Yiyang, and Yang Zhenli. "Study on Initial Gravity Map Matching Technique Based on Triangle Constraint Model." Journal of Navigation 69, no. 2 (September 21, 2015): 353–72. http://dx.doi.org/10.1017/s0373463315000661.
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In this paper, a gravity map-matching algorithm is proposed based on a triangle constraint model. A high-accuracy triangle constraint model is constructed by using a short time and high-accuracy-featured inertial navigation system. In this paper, the principle of the gravity map-matching algorithm based on the triangle constraint model and a triangle matching parameter-parsing method are first introduced in detail. It is verified by test that the method is sensitive to the initial error value. By comparison to the commonly used Iterative Closest Contour Point (ICCP) and Sandia Inertial Terrain Aided Navigation (SITAN) algorithms respectively, the results show that this method is perfect in real-time performance and reliability, and its advantages are more obvious especially with a large initial error.
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Fernandez, Raul, Andres S. Vazquez, Ismael Payo, and Antonio Adan. "A Comparison of Tactile Sensors for In-Hand Object Location." Journal of Sensors 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2943610.
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This work presents an extensive analysis of the usefulness of tactile sensors for in-hand object localization. Our analysis is based on a previous work where we proposed a method for the evaluation of tactile data using two algorithms: a Particle Filter algorithm and an Iterative Closest Point algorithm. In particular, we present a comparison of six different sensors, including two pairs of sensors based on similar technology, showing how the design and distribution of tactile sensors can affect the performance. Also, together with previous results where we demonstrated the importance of the synergy between tactile data and hand geometry, we corroborate that it is possible to obtain more similar performance with a simple fingertip sensor, than with more complex and expensive tactile sensors.
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Bolkas, D., G. Walton, R. Kromer, T. Sichler, and L. Weidner. "A NOVEL APPROACH TO REGISTER MULTI-PLATFORM POINT CLOUDS FOR ROCKFALL MONITORING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 693–700. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-693-2021.
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Abstract. Point cloud produced from technologies such as terrestrial laser scanning (TLS) and photogrammetry (terrestrial and aerial) are widely used in rockfall monitoring applications due to the wealth of data they provide. In such applications, the acquisition and registration of multi-epoch point clouds is necessary. In addition, point clouds can be derived from different sensors (e.g., lasers versus digital cameras) and different platforms (terrestrial versus aerial). Therefore, registration methods should be able to support multi-platform datasets. Currently, registration of multi-platform datasets is done with manual intervention, and automatic registration is difficult. While registration of TLS point clouds can be achieved by targets that are not on the rock surface, this is not the case for photogrammetric methods, as ground control points (GCPs) should be located on the rock surface. Such GCPs can be lost or destroyed with time, and re-establishing them is difficult. Automated registration often relies on feature-based algorithms with refinement using the iterative closest point (ICP) algorithm. This paper presents a novel registration approach of multi-epoch and multi-platform point clouds to support rockfall monitoring applications. The registration method is based on edges that are detected in the different datasets using α-molecules. The paper shows application examples of the novel approach at different rock slopes in Colorado. Results demonstrate that the developed method in many cases performs better than the well-known ICP method and can be used to register point clouds and support rockfall monitoring.
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Jang, Seong-Wook, Young-Jin Seo, Yon-Sik Yoo, and Yoon Sang Kim. "Computed Tomographic Image Analysis Based on FEM Performance Comparison of Segmentation on Knee Joint Reconstruction." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/235858.
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The demand for an accurate and accessible image segmentation to generate 3D models from CT scan data has been increasing as such models are required in many areas of orthopedics. In this paper, to find the optimal image segmentation to create a 3D model of the knee CT data, we compared and validated segmentation algorithms based on both objective comparisons and finite element (FE) analysis. For comparison purposes, we used 1 model reconstructed in accordance with the instructions of a clinical professional and 3 models reconstructed using image processing algorithms (Sobel operator, Laplacian of Gaussian operator, and Canny edge detection). Comparison was performed by inspecting intermodel morphological deviations with the iterative closest point (ICP) algorithm, and FE analysis was performed to examine the effects of the segmentation algorithm on the results of the knee joint movement analysis.
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Zhou, K., B. Gorte, and S. Zlatanova. "EXPLORING REGULARITIES FOR IMPROVING FAÇADE RECONSTRUCTION FROM POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 749–55. http://dx.doi.org/10.5194/isprsarchives-xli-b5-749-2016.
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(Semi)-automatic facade reconstruction from terrestrial LiDAR point clouds is often affected by both quality of point cloud itself and imperfectness of object recognition algorithms. In this paper, we employ regularities, which exist on façades, to mitigate these problems. For example, doors, windows and balconies often have orthogonal and parallel boundaries. Many windows are constructed with the same shape. They may be arranged at the same lines and distance intervals, so do different windows. By identifying regularities among objects with relatively poor quality, these can be applied to calibrate the objects and improve their quality. The paper focuses on the regularities among the windows, which is the majority of objects on the wall. Regularities are classified into three categories: within an individual window, among similar windows and among different windows. Nine cases are specified as a reference for exploration. A hierarchical clustering method is employed to identify and apply regularities in a feature space, where regularities can be identified from clusters. To find the corresponding features in the nine cases of regularities, two phases are distinguished for similar and different windows. In the first phase, ICP (iterative closest points) is used to identify groups of similar windows. The registered points and a number of transformation matrices are used to identify and apply regularities among similar windows. In the second phase, features are extracted from the boundaries of the different windows. When applying regularities by relocating windows, the connections, called chains, established among the similar windows in the first phase are preserved. To test the performance of the algorithms, two datasets from terrestrial LiDAR point clouds are used. Both show good effects on the reconstructed model, while still matching with original point cloud, preventing over or under-regularization.
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Zhou, K., B. Gorte, and S. Zlatanova. "EXPLORING REGULARITIES FOR IMPROVING FAÇADE RECONSTRUCTION FROM POINT CLOUDS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B5 (June 16, 2016): 749–55. http://dx.doi.org/10.5194/isprs-archives-xli-b5-749-2016.
Full textAbstract:
(Semi)-automatic facade reconstruction from terrestrial LiDAR point clouds is often affected by both quality of point cloud itself and imperfectness of object recognition algorithms. In this paper, we employ regularities, which exist on façades, to mitigate these problems. For example, doors, windows and balconies often have orthogonal and parallel boundaries. Many windows are constructed with the same shape. They may be arranged at the same lines and distance intervals, so do different windows. By identifying regularities among objects with relatively poor quality, these can be applied to calibrate the objects and improve their quality. The paper focuses on the regularities among the windows, which is the majority of objects on the wall. Regularities are classified into three categories: within an individual window, among similar windows and among different windows. Nine cases are specified as a reference for exploration. A hierarchical clustering method is employed to identify and apply regularities in a feature space, where regularities can be identified from clusters. To find the corresponding features in the nine cases of regularities, two phases are distinguished for similar and different windows. In the first phase, ICP (iterative closest points) is used to identify groups of similar windows. The registered points and a number of transformation matrices are used to identify and apply regularities among similar windows. In the second phase, features are extracted from the boundaries of the different windows. When applying regularities by relocating windows, the connections, called chains, established among the similar windows in the first phase are preserved. To test the performance of the algorithms, two datasets from terrestrial LiDAR point clouds are used. Both show good effects on the reconstructed model, while still matching with original point cloud, preventing over or under-regularization.
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Müller, Andreas Michael, and Tino Hausotte. "Data fusion of surface data sets of X-ray computed tomography measurements using locally determined surface quality values." Journal of Sensors and Sensor Systems 7, no. 2 (October 12, 2018): 551–57. http://dx.doi.org/10.5194/jsss-7-551-2018.
Full textAbstract:
Abstract. X-ray computed tomography as a measurement system faces some difficulties concerning the quality of the acquired measurements due to energy-dependent interaction of polychromatic radiation with the examined object at hand. There are many different techniques to reduce the negative influences of these artefact phenomena, which is also the aim of this newly introduced method. The key idea is to create several measurements of the same object, which only differ in their orientation inside the ray path of the measurement system. These measurements are then processed to selectively correct faulty surface regions. To calculate the needed geometrical transformations between the different measurements with the goal of a congruent alignment in one coordinate system, an extension of the iterative closest point (ICP) algorithm is used. To quantitatively classify any surface point regarding its quality value to determine the individual need of correction for each point, the local quality value (LQV) method is used, which has been developed at the Institute of Manufacturing Metrology. Different data fusion algorithms are presented whose performances are tested and verified using nominal–actual comparisons.
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Hsieh, Cheng Tiao. "A New Kinect-Based Scanning System and its Application." Applied Mechanics and Materials 764-765 (May 2015): 1375–79. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.1375.
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This paper aims at presenting a simple approach utilizing a Kinect-based scanner to create models available for 3D printing or other digital manufacturing machines. The outputs of Kinect-based scanners are a depth map and they usually need complicated computational processes to prepare them ready for a digital fabrication. The necessary processes include noise filtering, point cloud alignment and surface reconstruction. Each process may require several functions and algorithms to accomplish these specific tasks. For instance, the Iterative Closest Point (ICP) is frequently used in a 3D registration and the bilateral filter is often used in a noise point filtering process. This paper attempts to develop a simple Kinect-based scanner and its specific modeling approach without involving the above complicated processes.The developed scanner consists of an ASUS’s Xtion Pro and rotation table. A set of organized point cloud can be generated by the scanner. Those organized point clouds can be aligned precisely by a simple transformation matrix instead of the ICP. The surface quality of raw point clouds captured by Kinect are usually rough. For this drawback, this paper introduces a solution to obtain a smooth surface model. Inaddition, those processes have been efficiently developed by free open libraries, VTK, Point Cloud Library and OpenNI.
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Yi, Jin, Shiqiang Zhang, Yueqi Cao, Erchuan Zhang, and Huafei Sun. "Rigid Shape Registration Based on Extended Hamiltonian Learning." Entropy 22, no. 5 (May 12, 2020): 539. http://dx.doi.org/10.3390/e22050539.
Full textAbstract:
Shape registration, finding the correct alignment of two sets of data, plays a significant role in computer vision such as objection recognition and image analysis. The iterative closest point (ICP) algorithm is one of well known and widely used algorithms in this area. The main purpose of this paper is to incorporate ICP with the fast convergent extended Hamiltonian learning (EHL), so called EHL-ICP algorithm, to perform planar and spatial rigid shape registration. By treating the registration error as the potential for the extended Hamiltonian system, the rigid shape registration is modelled as an optimization problem on the special Euclidean group S E ( n ) ( n = 2 , 3 ) . Our method is robust to initial values and parameters. Compared with some state-of-art methods, our approach shows better efficiency and accuracy by simulation experiments.
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KOIVUNEN, V., and J. M. VEZIEN. "MACHINE VISION TOOLS FOR CAGD." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 02 (March 1996): 165–82. http://dx.doi.org/10.1142/s0218001496000141.
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In this paper, the problem of constructing geometric models from data provided by 3-D imaging sensors is addressed. Such techniques allow for rapid modeling of sculptured free-form shapes and generation of geometric models for existing parts. In order for a complete data set to be obtained, multiple images, each from a different viewpoint, have to be merged. A technique stemming from the Iterative Closest Point (ICP) method for estimating the relative transformations among the viewpoints is developed. Computational solutions are provided for estimating shape from noisy sensory measurements using representations that conform with commonly used representations from Computer Aided Geometric Design (CAGD). In particular, NURBS and triangular surface representations are applied in shape estimation. The surface approximations are refined by the algorithms to meet a user-defined tolerance value.
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Wang, Cheng-Wei, and Chao-Chung Peng. "3D Face Point Cloud Reconstruction and Recognition Using Depth Sensor." Sensors 21, no. 8 (April 7, 2021): 2587. http://dx.doi.org/10.3390/s21082587.
Full textAbstract:
Facial recognition has attracted more and more attention since the rapid growth of artificial intelligence (AI) techniques in recent years. However, most of the related works about facial reconstruction and recognition are mainly based on big data collection and image deep learning related algorithms. The data driven based AI approaches inevitably increase the computational complexity of CPU and usually highly count on GPU capacity. One of the typical issues of RGB-based facial recognition is its applicability in low light or dark environments. To solve this problem, this paper presents an effective procedure for facial reconstruction as well as facial recognition via using a depth sensor. For each testing candidate, the depth camera acquires a multi-view of its 3D point clouds. The point cloud sets are stitched for 3D model reconstruction by using the iterative closest point (ICP). Then, a segmentation procedure is designed to separate the model set into a body part and head part. Based on the segmented 3D face point clouds, certain facial features are then extracted for recognition scoring. Taking a single shot from the depth sensor, the point cloud data is going to register with other 3D face models to determine which is the best candidate the data belongs to. By using the proposed feature-based 3D facial similarity score algorithm, which composes of normal, curvature, and registration similarities between different point clouds, the person can be labeled correctly even in a dark environment. The proposed method is suitable for smart devices such as smart phones and smart pads with tiny depth camera equipped. Experiments with real-world data show that the proposed method is able to reconstruct denser models and achieve point cloud-based 3D face recognition.
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Chen, Yun Fang, Ruo Fei Zhong, and Bo Shi. "Integration Matching Algorithm on Gravity Aided Underwater Navigation." Applied Mechanics and Materials 137 (October 2011): 128–33. http://dx.doi.org/10.4028/www.scientific.net/amm.137.128.
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Marine gravity aided INS navigation, in which Gravity anomaly and gravity gradient of earth gravity field from sensors sensitive to gravity have been supplementary information aided to correct positioning error accumulated with time to achieve precise navigation and graphic trace, has been a fervent and frontier issue in underwater passive navigation technology in application to Autonomous Underwater Vehicle (AUV). Core theoretical technology of gravity aided INS navigation was matching algorithm, in which Terrain Contour Matching (TERCOM), Sandia Inertial Terrain Aided Navigation (SITAN) and Iterative Closest Contour Point (ICCP) were three typical representatives based on gravity reference map. However, these algorithms more or less had some limitations in certain conditions such as a relatively larger INS positioning error. Aiming at the application limitation that positioning error from INS were commonly large when after a long voyage AUVs entered into the region in which gravity could be used to matching, above three algorithms were integrated to use on base of expounding their algorithm models, computation progress and advantages and disadvantages analysis, then integration theory, algorithm design and model foundation were presented to meet practice application demand better.
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González-Pérez, Sara, Daniel Perea Ström, Natalia Arteaga-Marrero, Carlos Luque, Ignacio Sidrach-Cardona, Enrique Villa, and Juan Ruiz-Alzola. "Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring." Sensors 21, no. 7 (March 24, 2021): 2264. http://dx.doi.org/10.3390/s21072264.
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This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms’ performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions.
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Kuçak, Ramazan Alper, Serdar Erol, and Bihter Erol. "An Experimental Study of a New Keypoint Matching Algorithm for Automatic Point Cloud Registration." ISPRS International Journal of Geo-Information 10, no. 4 (March 31, 2021): 204. http://dx.doi.org/10.3390/ijgi10040204.
Full textAbstract:
Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.
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Chang, Wen Chung, and Chia Hung Wu. "Automated Bin-Picking with Active Vision." Key Engineering Materials 625 (August 2014): 496–504. http://dx.doi.org/10.4028/www.scientific.net/kem.625.496.
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In this research, an automated robotic bin-picking system employing active vision for picking up randomly distributed plumbing parts is presented. This system employs an actively-controlled single eye-in-hand system to observe structured light projected onto a set of plumbing parts in a bin. By using image processing and iterative closest point (ICP) algorithms, a single plumbing part that could possibly be taken from the bin is detected. Specifically, by projecting stationary structured light patterns onto the set of plumbing objects, the features on the surfaces of plumbing parts can be reconstructed by actively moving the eye-in-hand camera while performing visual tracking of those features. An effective 3D segmentation technique is employed to extract the point cloud of a single plumbing part that can possibly be grasped successfully. Once the object point cloud is obtained, one needs to determine the coordinate transformation from the end-effector to the selected plumbing part for grasping motion. With the point cloud matching result based on utilizing the ICP algorithm, the position and orientation of the selected plumbing part can be correctly estimated if the deviation of the object point cloud from the model point cloud is small. The control command can thus be given to the robotic manipulator to accomplish the automated bin-picking task. To effectively expand the allowed deviation of the object point cloud, an approximate pose estimation algorithm is employed before performing the ICP algorithm. The proposed approach can virtually estimate any pose of the plumbing part and has been successfully experimented with an industrial manipulator equipped with eye-in-hand single-camera vision and a LCD projector fixed in the work space demonstrating the feasibility and effectiveness. The proposed automated bin-picking system appears to be cost-effective and have great potentials in industrial factory automation applications.
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Lyda, A. W., X. Zhang, C. L. Glennie, K. Hudnut, and B. A. Brooks. "AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 35–42. http://dx.doi.org/10.5194/isprsarchives-xli-b2-35-2016.
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Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.
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Lyda, A. W., X. Zhang, C. L. Glennie, K. Hudnut, and B. A. Brooks. "AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 35–42. http://dx.doi.org/10.5194/isprs-archives-xli-b2-35-2016.
Full textAbstract:
Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.
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Bonnin-Pascual, Francisco, and Alberto Ortiz. "UWB-Based Self-Localization Strategies: A Novel ICP-Based Method and a Comparative Assessment for Noisy-Ranges-Prone Environments." Sensors 20, no. 19 (October 1, 2020): 5613. http://dx.doi.org/10.3390/s20195613.
Full textAbstract:
Ultra-Wide-Band (UWB) positioning systems are now a real option to estimate the position of generic agents (e.g., robots) within indoor/GPS-denied environments. However, these environments can comprise metallic structures or other elements which can negatively affect the signal transmission and hence the accuracy of UWB-based position estimations. Regarding this fact, this paper proposes a novel method based on point-to-sphere ICP (Iterative Closest Point) to determine the 3D position of a UWB tag. In order to improve the results in noise-prone environments, our method first selects the anchors’ subset which provides the position estimate with least uncertainty (i.e., largest agreement) in our approach. Furthermore, we propose a previous stage to filter the anchor-tag distances used as input of the ICP stage. We also consider the addition of a final step based on non-linear Kalman Filtering to improve the position estimates. Performance results for several configurations of our approach are reported in the experimental results section, including a comparison with the performance of other position-estimation algorithms based on trilateration. The experimental evaluation under laboratory conditions and inside the cargo hold of a vessel (i.e., a noise-prone scenario) proves the good performance of the ICP-based algorithm, as well as the effects induced by the prior and posterior filtering stages.
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Bouaziz, Sofien, Andrea Tagliasacchi, and Mark Pauly. "Sparse Iterative Closest Point." Computer Graphics Forum 32, no. 5 (August 2013): 113–23. http://dx.doi.org/10.1111/cgf.12178.
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Moravec, Jerry. "Hand contour classification using evolutionary algorithm." Information Technology And Control 49, no. 1 (March 25, 2020): 55–79. http://dx.doi.org/10.5755/j01.itc.49.1.24140.
Full textAbstract:
A biometric identification of persons wchich utilize contour of a human hand belogs to still very interesting and still not totally explored areas and its accuracy and effectiveness depends on technical capabilities to some extent. Presented paper solves given problem using combination of different algorithms. A hand contour is used, topological description of the hand, evolutionary algorithm, algorithm linear regression to estimate the knuckles positions and for contours comparison is used an algorithm Iterative Closest Point (ICP) in its genuine shape. All 5 fingers is at computer classification fully moveable, thumb has 2 knuckles. Modern evolutionary optimizers enable markedly to cut down computational demands of the algorithm ICP. Experimental verification of proposed recipes were performed with use of two different databases named THID and GPDS with persons of both gender and different age (cca 20-65let) with total number of oeprons in individual database 104 and 94. Experimental results checked succesfuly suitability of use combination of methods ICP and evolutionary optimizer which is named as EPSDE for solving of the given task with algorithmic complexity O(N) and success rate give by coefficient THID:EER=0.38% and GPDS:EER=0.35% on real images.
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Liu, Hui, Feng Lin, Jian-Li Yang, Hong-Rui Wang, and Xiu-Ling Liu. "Applying Side-chain Flexibility in Motifs for Protein Docking." Genomics Insights 8 (January 2015): GEI.S29821. http://dx.doi.org/10.4137/gei.s29821.
Full textAbstract:
Conventional rigid docking algorithms have been unsatisfactory in their computational results, largely due to the fact that protein structures are flexible in live environments. In response, we propose to introduce the side-chain flexibility in protein motif into the docking. First, the Morse theory is applied to curvature labeling and surface region growing, for segmentation of the protein surface into smaller patches. Then, the protein is described by an ensemble of conformations that incorporate the flexibility of interface side chains and are sampled using rotamers. Next, a 3D rotation invariant shape descriptor is proposed to deal with the flexible motifs and surface patches; thus, pairwise complementarity matching is needed only between the convex patches of ligand and the concave patches of receptor. The iterative closest point (ICP) algorithm is implemented for geometric alignment of the two 3D protein surface patches. Compared with the fast Fourier transform-based global geometric matching algorithm and other methods, our FlexDock system generates much less false-positive docking results, which benefits identification of the complementary candidates. Our computational experiments show the advantages of the proposed flexible docking algorithm over its counterparts.
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Jilani, Musfira, Padraig Corcoran, and Peter Mooney. "Lampposts as Landmarks for Simultaneous Localization and Mapping." Advanced Materials Research 403-408 (November 2011): 823–29. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.823.
Full textAbstract:
This paper investigates the effectiveness of using lampposts, which are commonly found in University campus environments with high frequency, as landmarks in a 2D LIDAR based Simultaneous Localization and Mapping (SLAM) framework. Lampposts offer a number of benefits compared to other forms of landmarks. Their unique spatial signature makes it possible to design effective algorithms to extract them. They have a very small spatial size. Their use removes the challenge of determining a corresponding location between difference views. This represents a major challenge if larger objects are used as landmarks. The proposed SLAM algorithm contains three stages. Firstly LIDAR segmentation is performed. Next each object is input to a binary classifier which determines objects with a high probability of corresponding to lampposts. Finally these extracted lampposts are input to an Iterative Closest Point (ICP) based SLAM algorithm. The ICP algorithm used is an extension of the traditional ICP algorithm and filters associations due to noise. Results achieved by the proposed system were very positive. An accurate map of a university’s lampposts was created and localization, when compared to GPS ground-truth, was very accurate.