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International Conference on Pattern Recognition

作者： D. Chetverikov D. Svirko D. Stepanov P. Krsek Computer and Automation Institute Budapest Hungary FEE Center for Applied Cybernetics Czech Technical University Czech Republic

ISBN: (纸本)076951695X

The problem of geometric alignment of two roughly preregistered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular iterative closest point (ICP) algorithm (Besl and McKay, 1992) is presented, called the Trimmed ICP (TrICP). The new algorithm is based on the consistent use of the least trimmed squares (LTS) approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50%, robust to erroneous measurements and shape defects, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100%. Results of testing the new algorithm are shown.

关键词： iterative closest point algorithm iterative algorithms Robustness Shape measurement Convergence Motion analysis Cost function Automation Testing Reverse engineering

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Registration of range data using a hybrid simulated annealing and iterative closest point algorithm

Registration of range data using a hybrid simulated annealin...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： J. Luck C. Little W. Hoff Colorado Schml of Mines Sandia National Laboratories

The need to register data is abundant in applications such as: world modeling, part inspection and manufacturing, object recognition, pose estimation, robotic navigation, and reverse engineering. Registration occurs by aligning the regions that are common to multiple images. The largest difficulty in performing this registration is dealing with outliers and local minima while remaining efficient. A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima. Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow. Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing. Additionally, a robust error function is incorporated to deal with outliers. This algorithm is incorporated into a complete modeling system that inputs two sets of range data, registers the sets, and outputs a composite model.

关键词： Simulated annealing iterative closest point algorithm iterative algorithms Solid modeling Laboratories Inspection Virtual manufacturing Object recognition Robots Navigation

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Improved iterative closest point algorithm using Truncated Signed Distance Function

Improved Iterative Closest Point Algorithm using Truncated S...

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International Conference on Control, Automation and Systems

作者： Hanjun Kim H. K. Hong B. H. Lee ASRI Department of Electrical and Computer Engineering Seoul National University Seoul Korea

ISBN: (纸本)9781538670804;9788993215168

Truncated signed distance is one of the methods to represent 3D spatial information by using the distance from the surface of an object, and is mainly used for 3D dense reconstruction. When using truncated signed distance value for 3D reconstruction, iterative closest point algorithm is generally used for matching two point clouds. However, iterative closest point algorithm for 3D reconstruction uses only the geometric information of point clouds themselves. Due to lack of information in the surface structure, it is possible to fall in the local minimum or occur convergence failure in iteration process. To solve these problems, we use additional information from truncated signed distance function values. As we optimize the joint objective function composed of the geometric information and the truncated signed distance function information, it is possible to register two point clouds to be more robust in iterative closest point algorithm. Experimental results demonstrate that the proposed method outperforms the conventional method using only a geometric information.

关键词： iterative closest point algorithm Truncated signed distance function 3D reconstruction 3D registration

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An Improved Semi-Dense iterative closest point Registration algorithm Fusing Intrinsic Shape Signatures and Curvature Feature

An Improved Semi-Dense Iterative Closest Point Registration ...

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Intelligent Computing and Robotics (ICICR), International Conference on

作者： Ye Li Rong Fei Tong Zhang Institute of Computer Science & Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798350372274

ISBN: (纸本)9798350372281

point clouds registration is a key step in three- dimension data processing and the iterative closest point algorithm has been widely for this task. An improved semi-dense iterative closest point registration algorithm fusing Intrinsic Shape Signatures and curvature feature is proposed in this paper to solve the problem of large-scale and small-overlap point clouds registration. Firstly, the semi-dense point cloud is generated by a voxel grid filter and the feature points of Intrinsic Shape Signatures are extracted from the semi-dense point cloud for coarse registration. Then in coarse registration step, the super 4-points Congruent Sets algorithm is used to get better registration results, even on partial overlapping point clouds. Finally, fine registration is completed by using an improved iterative closest point algorithm based on curvature feature and K-D tree. Experiments on indoor and outdoor 3D point cloud data show that the proposed algorithm in this paper is more accurate, efficient and robust compared with the traditional ICP algorithm, especially for large-scale and small-overlap point clouds.

关键词： point cloud compression Solid modeling Three-dimensional displays Accuracy Shape iterative closest point algorithm Feature extraction

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Precise 2D point Set Registration Using iterative closest algorithm and Correntropy

Precise 2D Point Set Registration Using Iterative Closest Al...

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International Joint Conference on Neural Networks

作者： Guanglin Xu Shaoyi Du Jianru Xue Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University 710049 China

ISBN: (纸本)9781509006212

The iterative closest point (ICP) algorithm is fast and accurate for rigid point set registration, but it works badly when there are many outliers and noises in the point sets. This paper instead proposes a novel method based on the ICP algorithm to deal with this problem. Firstly, correntropy is introduced into the rigid registration problem and then a new energy function based on maximum correntropy criterion is proposed. After that, a new ICP algorithm based on correntropy is proposed, which performs well in dealing with rigid registration with noises and outliers. This new algorithm converges moronically from any given parameters, which is similar to the ICP algorithm. Experimental results demonstrate its accuracy and efficiency compared with the traditional ICP algorithm.

关键词： iterative closest point algorithm algorithm design and analysis Data models Two dimensional displays Robustness Euclidean distance Mathematical model iterative closest point algorithm algorithm design and analysis data models Euclidean distance Mathematical Model Inductively coupled plasma Noise Set of points Registration

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Improved iterative closest point (ICP) point Cloud Registration algorithm based on Matching point Pair Quadratic Filtering

Improved Iterative Closest Point (ICP) Point Cloud Registrat...

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Computer, Internet of Things and Control Engineering (CITCE), International Conference on

作者： JiaWei Yu CunGui Yu ChaoDong Lin FuQuan Wei School of Mechanical Engineering Nanjin University of Science and Technology Nanjin China

ISBN: (纸本)9781665421850

Aiming at the problems of long computing time and poor registration accuracy in current point cloud registration, an improved ICP algorithm based on matching point pair secondary filtering was proposed, which combined ground segmentation and point cloud filtering algorithm for pre-processing. Firstly, ground segmentation is performed on the point cloud data obtained by Lidar, and ground points are filtered. Next, Kdtree_ICP is used for point cloud registration, and the abnormal matching point pairs obtained by Kdtree search are filtered during the matching process. Finally, the point cloud data of outdoor ground is used for experimental verification. Experimental results show that the proposed method greatly improves the computational speed and accuracy, and the algorithm is stable and reliable.

关键词： point cloud compression Matched filters Laser radar Control engineering Filtering iterative closest point algorithm Filtering algorithms

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Solution of The Variational Registration Problem Based on iterative closest point algorithms for Various Types of Geometric Transformations

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JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2022年第SUPPL 1期67卷 S119-S128页

作者： Makovetskii, A. Yu. Kober, V. I. Voronin, S. M. Voronin, A. V. Karnaukhov, V. N. Mozerov, M. G. Chelyabinsk State Univ Chelyabinsk 454001 Russia Russian Acad Sci Inst Informat Transmiss Problems Kharkevich Inst Moscow 127051 Russia Ctr Sci Res & Higher Educ Ensenada 22860 BC Mexico

The most popular algorithm for registering clouds of points in 3D space is the iterative closest point (ICP) algorithm. The point-to-point variational problem for orthogonal transformations is mathematically equivalent to the absolute orientation problem in photogrammetry. In this paper, we briefly overview closed-form solutions to the point-to-point variational problem. The well-known Horn algorithm solves the problem for the O(3) group. We propose a modified Horn algorithm that makes it possible to solve the problem for the SO(3) group. Computer simulation illustrates the registration accuracy of the considered methods for various geometric transformations.

关键词： Keywords absolute orientation problem iterative closest point algorithm point-to-point matching closed-form solution exact solution orthogonal transformation affine transformation

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Evaluation of the ICP algorithm in 3D point Cloud Registration

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IEEE ACCESS 2020年 8卷 68030-68048页

作者： Li, Peng Wang, Ruisheng Wang, Yanxia Tao, Wuyong Chuzhou Univ Sch Geog Informat & Tourism Chuzhou 239000 Peoples R China Univ Calgary Dept Geomat Engn Calgary AB T2N 1N4 Canada Guangzhou Univ Sch Geog Sci Guangzhou 510006 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China

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

关键词： iterative closest point algorithm Three-dimensional displays Robustness Convergence Shape Gaussian noise Search problems ICP point cloud registration validity accuracy efficiency

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Registration of Multi-View point Sets Under the Perspective of Expectation-Maximization

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 9176-9189页

作者： Zhu, Jihua Guo, Rui Li, Zhongyu Zhang, Jing Pang, Shanmin Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Zhongnan Univ Econ & Law Sch Math & Stat Wuhan 430073 Peoples R China

Multi-view registration plays a critical role in 3D model reconstruction. To solve this problem, most previous methods align point sets by either partially exploring available information or blindly utilizing unnecessary information, which may lead to undesired results or extra computation complexity. Accordingly, we propose a novel solution for the multi-view registration under the perspective of Expectation-Maximization (EM). The proposed method assumes that each data point is generated from one unique Gaussian Mixture Model (GMM), where its corresponding points in other point sets are regarded as Gaussian centroids with equal covariance and membership probabilities. As it is difficult to obtain real corresponding points in the registration problem, they are approximated by the nearest neighbor in each other aligned point sets. Based on this assumption, it is reasonable to define the likelihood function including all rigid transformations, which require to be estimated for multi-view registration. Subsequently, the EM algorithm is derived to estimate rigid transformations with one Gaussian covariance by maximizing the likelihood function. Since the GMM component number is automatically determined by the number of point sets, there is no trade-off between registration accuracy and efficiency in the proposed method. Finally, the proposed method is tested on several benchmark data sets and compared with state-of-the-art algorithms. Experimental results demonstrate its superior performance on the accuracy, efficiency, and robustness for multi-view registration.

关键词： iterative closest point algorithm Robustness Three-dimensional displays Solid modeling Gaussian mixture model Computational modeling Gaussian distribution Gaussian mixture model expectation-maximization point set registration

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Robust point Set Registration Using Signature Quadratic Form Distance

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第5期50卷 2097-2109页

作者： Li, Liang Yang, Ming Wang, Chunxiang Wang, Bing Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

point set registration is a problem with a long history in many pattern recognition tasks. This paper presents a robust point set registration algorithm based on optimizing the distance between two probability distributions. A major problem in point to point algorithms is defining the correspondence between two point sets. This paper follows the idea of some probability-based point set registration methods by representing the point sets as Gaussian mixture models (GMMs). By optimizing the distance between the two GMMs, rigid transformations (rotation and translation) between two point sets can be obtained without having to find a correspondence. Previous studies have used L2, Kullback Leibler, etc. distance to measure similarity between two GMMs;however, these methods have problems with robustness to noise and outliers, especially when the covariance matrix is large, or a local minimum exists. Therefore, in this paper, the signature quadratic form distance is derived to measure the distribution similarity. The contribution of this paper lies in adopting the signature quadratic form distance for the point set registration algorithm. The experimental results show the precision and robustness of this algorithm and demonstrate that it outperforms other state-of-the-art point set registration algorithms regarding factors, such as noise, outliers, missing partial structures, and initial misalignment.

关键词： Robustness iterative closest point algorithm Optimization Gaussian distribution Three-dimensional displays Linear programming Measurement Gaussian mixture model (GMM) point cloud matching point set registration rigid registration signature quadratic form distance

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