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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

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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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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Map building of uncertain environment based on iterative closest point algorithm on the cloud

Map building of uncertain environment based on iterative clo...

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International Conference on Mechatronics (ICM)

作者： Yi-Jou Wen Chen-Chien Hsu Wei-Yen Wang Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan R.O.C.

ISBN: (纸本)9781479936342

The iterative closest point (ICP) algorithm is to align for the two point sets, which is widely used in map building of an uncertain environment. However, the original ICP algorithm is easily affected by noise and discrete points, making the error of alignment very large. At the same time, in a row scanning by the Laser Range Finder (LRF), the more data points accumulate, the larger the errors of alignment become, which leads to an unpreferable map, and the process would be time consuming. This paper proposes a map building of an uncertain environment based on an enhanced ICP (E-ICP) algorithm on the cloud, called E-ICP on the cloud, and presented a way to reduce duplicate reference point set. Thus, one can significantly reduce the computational burden, improve the accuracy of alignment, and get a more accurate environmental map.

关键词： iterative closest point algorithm Robots Buildings Parallel processing Noise Accuracy Lasers

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Application of iterative closest point algorithm in Automatic Flight of Speedy UAV 6

Application of Iterative Closest Point Algorithm in Automati...

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IEEE Chinese Guidance, Navigation and Control Conference (CGNCC)

作者： Gu Tianyuan Zhang Ning AVIC Xian Flight Automat Control Res Inst Sci & Technol Aircraft Control Lab Xian 710065 Peoples R China

ISBN: (纸本)9781479946990

In order to realize the autonomous location and attitude estimation for speedy UAV, this paper designs an improved iterative closest points (ICP) algorithm for fixed-wing UAV with a laser range finder mounted onboard. This improved ICP adopts a K-d tree and AK-d tree mixed closest point searching strategy to enhance its rapidity, and an automatic trim approach to drop out outlier points to improve its reliability. In order to check the feasibility of the proposed algorithm, flight experiment is conducted. Experiment result demonstrates the good performance of the algorithm.

关键词： attitude control autonomous aerial vehicles iterative methods position control trees (mathematics) AK-d tree ICP algorithm K-d tree attitude estimation automatic UAV flight automatic trim approach closest point searching strategy fixed-wing UAV iterative closest point algorithm laser range finder location estimation speedy UAV unmanned aerial vehicle algorithm design and analysis Estimation iterative closest point algorithm Laser radar Reliability Sensors Standards iterative closest point algorithm laser range finders algorithm design and analysis Lidar Unmanned Aerial Vehicle drone honey bees position estimation attitude control Position control iterative methods fixed wings Sensors

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CHANGE DETECTION FROM DIFFERENTIAL AIRBORNE LIDAR USING A WEIGHTED ANISOTROPIC iterative closest point algorithm

CHANGE DETECTION FROM DIFFERENTIAL AIRBORNE LIDAR USING A WE...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiao Zhang Craig Glennie University of Houston

Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications, such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change from LiDAR is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon time varying factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anisotropic weighted ICP (A-ICP) algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing point clouds with synthetic fault ruptures between a uniform weight and anistropically weighted ICP algorithm. Then post-earthquake slip is estimated for the 2010 ElMayor-Cucapah Earthquake (EMC), using pre- and post-event LiDAR. Based on the analysis, Moving Window A-ICP is able to better estimate the synthetic surface ruptures, and provides a smoother estimate of actual displacement for the EMC earthquake.

关键词： Laser radar iterative closest point algorithm Three-dimensional displays Accuracy Earthquakes Estimation Electromagnetic compatibility

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Probability iterative closest point algorithm for position estimation

Probability iterative closest point algorithm for position e...

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International Conference on Intelligent Transportation

作者： Juan Liu Shaoyi Du Chunjia Zhang Jihua Zhu Ke Li Jianru Xue Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi Province China School of Software Engineering Xi'an Jiaotong University Xi'an Shaanxi Province China Fundamental Science on Ergonomics and Environment Control Laboratory Beihang University Beijing China

ISBN: (纸本)9781479960798

This paper proposes probability iterative closest point (ICP) method based on expectation maximization (EM) estimation for point set registration with noise. The classical ICP algorithm can deal with rigid registration between two point sets effectively, but always fails to register point sets with noise. In order to improve the registration precision, a Gaussian model is introduced into the rigid registration. In each iteration, the classical ICP algorithm includes two steps, building the corresponding relationship and computing the rigid transformation. Similar to the traditional ICP, at each step, firstly the corresponding relationship is set up. Secondly, the rigid transformation is solved by singular value decomposition (SVD) method, and then the Gaussian model is updated by the distance and variance between two point sets. The experimental results on part B of CE-Shape-1 database and real position dataset validate that the proposed algorithm is more accurate.

关键词： iterative closest point algorithm Noise Shape Noise measurement Estimation Educational institutions Vectors

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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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point Set Registration Based on Adaptive Maximum Correntropy 25

Point Set Registration Based on Adaptive Maximum Correntropy

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Shao, Yinfeng Cui, Wenting Xu, Liang Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350366600

Aiming at the problem that the iterative closest point algorithm has a lot of noise in data and poor robustness to outliers, this paper proposes a point set registration algorithm based on the adaptive maximum correntropy criterion. The aim is to enhance the robustness of traditional registration methods, improve registration accuracy, and reduce registration time. Firstly, the maximum correntropy criterion is introduced to effectively identify and eliminate noise and outliers in the data. Secondly, a parameter-adaptive iterative approach is proposed to iteratively remove noise and outliers in the data, thus improving the accuracy and robustness of point set registration. Experimental results on datasets containing a large amount of noise and outliers demonstrate that the proposed algorithm effectively enhances the accuracy and robustness of point set registration. © 2024 IEEE.

关键词： Adaptive iterative closest point algorithm Maximum correntropy criterion Robustness

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Fast and Robust iterative closest point

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第7期44卷 3450-3466页

作者： Zhang, Juyong Yao, Yuxin Deng, Bailin Univ Sci & Technol China Sch Math Sci Hefei 230052 Anhui Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for rigid registration between two point sets, with wide applications in different areas from robotics to 3D reconstruction. The main drawbacks for ICP are its slow convergence, as well as its sensitivity to outliers, missing data, and partial overlaps. Recent work such as Sparse ICP achieves robustness via sparsity optimization at the cost of computational speed. In this paper, we propose a new method for robust registration with fast convergence. First, we show that the classical point-to-point ICP can be treated as a majorization-minimization (MM) algorithm, and propose an Anderson acceleration approach to speed up its convergence. In addition, we introduce a robust error metric based on the Welsch's function, which is minimized efficiently using the MM algorithm with Anderson acceleration. On challenging datasets with noises and partial overlaps, we achieve similar or better accuracy than Sparse ICP while being at least an order of magnitude faster. Finally, we extend the robust formulation to point-to-plane ICP, and solve the resulting problem using a similar Anderson-accelerated MM strategy. Our robust ICP methods improve the registration accuracy on benchmark datasets while being competitive in computational time.

关键词： Convergence Acceleration Three-dimensional displays Measurement Optimization Robustness iterative closest point algorithm Rigid registration robust estimator fixed-point iterations majorlazer minimization method anderson acceleration

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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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