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International Joint Conference on Neural Networks (IJCNN)

作者： Wu, Zongze Chen, Hongchen Du, Shaoyi South China Univ Technol Sch Elect & Informat Engn Guangzhou Guangdong Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China

ISBN: (纸本)9781509006199

The traditional affine iterative closest point (ICP) algorithm is fast and accuracy for affine registration of point sets, but it performs worse when the point sets with large outliers. This paper introduces a novel algorithm based on correntropy for affine registration of point sets with outliers. First, a novel objective function is proposed by introducing the maximum correntropy criterion (MCC) because of the outlier-rejection property of correntropy. Then, a new affine ICP algorithm is proposed to solve this energy function. This method uses a simple iterative algorithm and computes the affine transformation quickly at each iterative step. Similar to the ICP algorithm, this new algorithm converges monotonically to a local maximum for any given initial parameters. Experimental results demonstrate that our algorithm has the high speed and accuracy for affine registration with outliers compared with the traditional ICP algorithm and the state-of-the-art algorithms.

关键词： iterative closest point algorithm correntropy outliers affine registration

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Improved iterative closest point algorithm using Truncated Signed Distance Function 18

Improved Iterative Closest Point Algorithm using Truncated S...

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18th International Conference on Control, Automation and Systems (ICCAS)

作者： Kim, Hanjun Hong, H. K. Lee, B. H. Seoul Natl Univ Dept Elect & Comp Engn ASRI Seoul 08826 South Korea

ISBN: (纸本)9788993215151

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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Morphological iterative closest point algorithm

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1999年第11期8卷 1644-1646页

作者： Kapoutsis, CA Vavoulidis, CP Pitas, I Aristotelian Univ Salonika Dept Informat GR-54006 Salonika Greece

This work presents a method for the registration of three-dimensional (3-D) shapes. The method is based on the iterative closest point (ICP) algorithm and improves it through the use of a 3-D volume containing the shapes to be registered. The Voronoi diagram of the "model" shape points is first constructed in the volume. Then this is used for the calculation of the closest point operator. This way a dramatic decrease of the computational cost is achieved.

关键词： iterative closest point algorithm Voronoi tesselation

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3D point cloud matching based on principal component analysis and iterative closest point algorithm

3D point cloud matching based on principal component analysi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Chi Yuan Xiaoqing Yu Ziyue Luo Institute of Smart City Shanghai University Shanghai China

ISBN: (纸本)9781509006557

point cloud matching is one of the key technologies of optical three-dimensional contour measurement. Most of the point cloud matching without landmark used the iterative closest point algorithm. In order to improve the performance of the iterative closest point algorithm, the two-step iterative closest point algorithm was proposed. The improved algorithm is divided into a rough matching step and accurate matching step. Rough matching used the principal component analysis algorithm, while the fine matching used the improved iterative closest point algorithm. Compared with the classic iterative closest point algorithm, the improved algorithm can match the partial coincident point cloud. At the same time, the experiment can validate the effectiveness of the proposed algorithm.

关键词： Three-dimensional displays iterative closest point algorithm algorithm design and analysis Convergence Quaternions Principal component analysis Transforms

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VICP: Velocity updating iterative closest point algorithm

VICP: Velocity updating iterative closest point algorithm

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Seungpyo Hong Heedong Ko Jinwook Kim Imaging Media Research Center Korea Institute of Science and Technology Seoul Seoul Korea

We propose a novel method to enhance a family of ICP(iterative closest point) algorithms by updating velocity. Even though ICP algorithms play a dominant role in a model based tracking, it is difficult to avoid an accumulated tracking error during a continuous motion. It is because that typical ICP algorithms assumes that each of the point in one scan are measured simultaneously while most of the available rangefinders measure each point sequentially. Hence conventional ICP algorithms are prone to be erroneous under a fast motion and an accumulated error during the motion cannot be ignored in many cases. In our approach, we estimate a velocity of a rangefinder numerically over ICP iterations. As a result, distortion of a scan due to the motion can be compensated using estimated velocity. In addition, outliers are effectively rejected during the iteration of velocity update, which means that more accurate and robust motion is trackable. Also we verify a performance and an accuracy of our method by demonstrating simulation and real-world experiment results.

关键词： iterative closest point algorithm iterative algorithms Tracking Distortion measurement Robot kinematics Mobile robots Robot sensing systems Laser noise Robotics and automation USA Councils

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Fast and robust isotropic scaling probability iterative closest point algorithm

Fast and robust isotropic scaling probability iterative clos...

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Chinese Automation Congress (CAC)

作者： Juan Liu Shaoyi Du Di Qu Jianru Xue Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China

This paper proposes a new probability iterative closest point approach with bounded scale based on expectation maximization (EM) estimation for scaling registration of point sets with noise. The bounded scale ICP algorithm can handle the case with different scales, but it could not effectively yield the alignment of point sets with noise. Aiming at improving the registration precision, a Gaussian probability model is integrated into the bounded scale registration. The proposed method can be solved by the E-step and M-step. In the E-step, we can build up the one-to-one correspondence between two point sets. In the M-step, the scale transformation which consists of the rotation matrix, translation vector, and the scale factor is solved by singular value decomposition (SVD) method and the properties of parabola. Then, the Gaussian model is updated via the distance and variance between the transformed point sets. As one-to-one correspondence is adopted for the scaling registration of point sets with noise, the proposed method improves the performance significantly with high precision and fast speed. Experimental results demonstrate that the proposed algorithm is more accurate and fast.

关键词： Shape iterative closest point algorithm Computational modeling Linear programming Noise measurement Estimation

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Robust Affine iterative closest point algorithm Based on Correntropy for 2D point Set Registration

Robust Affine Iterative Closest Point Algorithm Based on Cor...

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International Joint Conference on Neural Networks

作者： Zongze Wu Hongchen Chen Shaoyi Du School of Electronic and Information Engineering South China University of Technology Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

ISBN: (纸本)9781509006212

关键词： iterative closest point algorithm Correntropy Outliers Affine registration

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Enhanced Indoor Mapping via Integration of Lidar and HoloLens 2 Data Using iterative closest point algorithm

Enhanced Indoor Mapping via Integration of Lidar and HoloLen...

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International Symposium on Intelligent Systems and Informatics (SISY)

作者： Panka Szalka Attila Hideg Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

This study presents an innovative method to improve the accuracy of indoor mapping by deploying and integrating a sensor that transmits two-dimensional data with a three-dimensional mesh created by another device. The iterative closest point algorithm was used to coordinate the different data sets and transform their different coordinate systems into a common indoor map. Prior to this, rigorous image cleaning and matching processes are conducted to ensure the quality of the data used. The effectiveness of this methodology has been validated through multiple tests using a Lidar scanner and a HoloLens 2 Augmented Reality device. This method not only offers potential for enhancing indoor navigation and safety protocols like emergency evacuation but also presents opportunities for advancing asset tracking and other applications.

关键词： Laser radar Accuracy iterative closest point algorithm Tracking Heuristic algorithms Transforms Robot sensing systems Real-time systems Cleaning Augmented reality

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A Fast Multi-Resolution iterative closest point algorithm

A Fast Multi-Resolution Iterative Closest Point Algorithm

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Chinese Conference on Pattern Recognition (CCPR)

作者： Ce Li Jianru Xue Shaoyi Du Nanning Zheng Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

iterative closest point (ICP) is a classical algorithm for rigid point set registration. However, with the number of points in the set increasing, its computational efficiency usually suffers a reduction, which limits the practical applications of this algorithm. Based on Weber's Law in psychophysics, this paper proposes a fast ICP algorithm based on hierarchical and multi-resolution model for point set matching. In the proposed algorithm, an initial transformation matrix between two point sets acquired firstly through the fast iterative computation at lower resolution, and then it is used as the initial value for a more precise registration at high resolution and we complete the overall rigid point set registration process. By this proposal, the time cost for computing the initial value at high resolution could be saved and thus the efficiency of the overall ICP registration algorithm would be greatly improved. Experimental results show that the proposed algorithm is apparently more efficient than the traditional ICP algorithm on both 2D and 3D point sets.

关键词： iterative closest point algorithm Shape algorithm design and analysis Three dimensional displays Computational modeling Data models Robustness

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Face Recognition With 3 Dimensional Profile by using iterative closest point algorithm

Face Recognition With 3 Dimensional Profile by using Iterati...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Ozgur Cevik Hasan Sakir Bilge Elektrik ve Elektronik Mühendisliği Bölümü Gazi Üniversitesi Ankara Elektrik ve Elektronik Mühendisliği Bölümü Gazi Üniversitesi Ankara Turkey

In this study, a face recognition method is presented, that is based on best matching after registration of a 3D profile taken from a test face with 3D point cloud of other faces in the database. For 3D registration, iterative closest point algorithm is used. After this operation, the best matching face is chosen according to their distances. Thus, there is no need for early registration of test face and the faces in the database. When there is a new test face, this procedure is applied and the necessary search for matching is done. At the result of experimental studies by using this approach, it is observed that a face can be successfully recognized with only one characteristic profile of it. Thus, it is shown that successful recognition can be done in lack of whole face 3D data. In this approach, the registration process is faster because instead of all points only a profile is used.

关键词： Face recognition iterative closest point algorithm Testing Linear discriminant analysis Databases Clouds Character recognition Gaussian processes

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