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International Conference on Control, Automation and Information Sciences (ICCAIS)

作者： Pablo Barrios Vicente Guzman Martin Adams Martin Rudorfer Dept. Electrical Eng. & AMTC Universidad de Chile Santiago Chile Dept. Electrical Eng. Universidad de Chile Santiago Chile Dept. Applied AI & Robotics Aston University Birmingham UK

ISBN: (数字)9798331542047

ISBN: (纸本)9798331542054

Ahstract- The registration of point cloud data is essential in various applications, such as computer vision and robotics. The iterative closest point (ICP) algorithm offered a solution to this problem, with several subsequent methods addressing problems including occlusions and variable point data overlap. To also account for detection errors, the Particle Swarm Optimization - Cardinalized Optimal Linear Assignment (PSO-COLA) point data registration algorithm was introduced. This algorithm offers robust registration solutions in the presence of data miss-detections and false alarms, but being based on a Particle Swarm Optimization (PSO) concept is susceptible to local minima problems. To address this problem, we propose the use of two additional meta-heuristic algorithms, namely Artificial Rabbit Optimisation (ARO) and Artificial Bee Colony (ABC), in combination with the Cardinalized Optimal Linear Assignment (COLA) metric. Our experiments show that the resulting ARO-COLA algorithm reduces the execution time compared with the former PSO-COLA algorithm while maintaining high registration accuracy, especially in scenarios with cardinality and spatial errors. The results indicate that the ARO-COLA algorithm is a promising alternative for efficient and accurate point cloud registration.

关键词： point cloud compression Measurement Rabbits Computer vision Accuracy Automation iterative closest point algorithm Metaheuristics iterative methods Particle swarm optimization

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An improved ICP registration algorithm for 2D point cloud

An improved ICP registration algorithm for 2D point cloud

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Duo Zhou Shibin Yin Hongbo Xie School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin China Isvision (Hangzhou) Technology Company Limited Hangzhou China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In this study, an improved iterative closest point (ICP) registration algorithm was proposed to address such problems as great time consumption of iterative calculations and relatively large registration errors in the traditional ICP algorithm within the field of 2D point cloud registration. During the iteration process, the point-to-line (PL) objective equation was simplified to solve the linear least squares fast through singular value decomposition, etc. Subsequently, this result could be further nonlinearly optimized, and the weighted objective functions for the PL distance and point-to-point (PP) distance were introduced in the optimization process. Moreover, a simulation experiment was performed using point clouds that were randomly generated multiple times. The results reveal that the improved ICP algorithm proposed in this study can improve both the registration accuracy and efficiency.

关键词： point cloud compression Accuracy iterative closest point algorithm Signal processing algorithms Signal processing Linear programming Mathematical models iterative methods Optimization Singular value decomposition

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Precise and Robust Color point Cloud Registration with Correntropy-Based ICP algorithm

Precise and Robust Color Point Cloud Registration with Corre...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yuhuan Fan Jing Ma The Experimental High School Attached to Beijing Normal University Beijing China

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

The paper proposes a variant of the iterative closest point (ICP) algorithm for point cloud registration that addresses two main issues with existing ICP-based algorithms: the lack of precise and complete color information and the lack of robustness to noise and outliers. The proposed algorithm introduces a color distance metric based on the L*a*b* color space and a correntropy-based objective function to enhance the accuracy and robustness of the algorithm. Simulation experiments on an RGB-D object dataset show that the proposed algorithm outperforms existing ICP-based methods in terms of accuracy and robustness.

关键词： point cloud compression Measurement Accuracy Image color analysis iterative closest point algorithm Big Data Linear programming

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point Set Registration Based on Adaptive Maximum Correntropy

Point Set Registration Based on Adaptive Maximum Correntropy

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System Simulation Technology and its Application (CCSSTA), IEEE China Conference on

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

ISBN: (数字)9798350366600

ISBN: (纸本)9798350366617

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.

关键词： Accuracy Adaptive systems iterative closest point algorithm Noise Birds Robustness Systems simulation Noise measurement iterative methods Clocks

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Benchmarking Classical and Learning-Based Multibeam point Cloud Registration

Benchmarking Classical and Learning-Based Multibeam Point Cl...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Li Ling Jun Zhang Nils Bore John Folkesson Anna Wåhlin Division of Robotics Perception and Learning (RPL) KTH Royal Institute of Technology Stockholm Sweden Institute of Computer Graphics and Vision (ICGV) TU Graz Austria Ocean Infinity Västra Frölunda Sweden Department of Marine Sciences University of Gothenburg Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Deep learning has shown promising results for multiple 3D point cloud registration datasets. However, in the underwater domain, most registration of multibeam echo-sounder (MBES) point cloud data are still performed using classical methods in the iterative closest point (ICP) family. In this work, we curate and release DotsonEast Dataset, a semi-synthetic MBES registration dataset constructed from an autonomous underwater vehicle in West Antarctica. Using this dataset, we systematically benchmark the performance of 2 classical and 4 learning-based methods. The experimental results show that the learning-based methods work well for coarse alignment, and are better at recovering rough transforms consistently at high overlap (20-50%). In comparison, GICP (a variant of ICP) performs well for fine alignment and is better across all metrics at extremely low overlap (10%). To the best of our knowledge, this is the first work to benchmark both learning-based and classical registration methods on an AUV-based MBES dataset. To facilitate future research, both the code and data are made available online. 1

关键词： point cloud compression Learning systems Measurement Three-dimensional displays Simultaneous localization and mapping iterative closest point algorithm Transforms

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point Cloud Registration Method Based on Bidirectional Search and Curvature Information

Point Cloud Registration Method Based on Bidirectional Searc...

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Chinese Automation Congress (CAC)

作者： Lingyun Yang Xinming Xie Jinhu Niu Aimin An School of Electrical and Information Engineering Lanzhou University of Technology LanZhou China Network Center Department Lanzhou University of Technology LanZhou China Gansu Guokai New Energy Equipment Co. LTD Baiyin China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper addresses the problem of registering unorganized point clouds without prior information and proposes a robust automatic point cloud registration method. This method is designed to overcome the sensitivity of the classical iterative closest point (ICP) algorithm to initial poses and its tendency to get trapped in local optima. The algorithm utilizes local information to filter feature points and construct feature descriptors, and then combines with the Random Sample Consensus (RANSAC) algorithm for coarse point cloud registration. The classical ICP algorithm is improved by using bidirectional search and constraints based on distance and curvature to obtain correct correspondences, achieving precise point cloud registration. Experimental results of point cloud registration demonstrate that this algorithm has significant advantages in terms of robustness and registration accuracy.

关键词： point cloud compression Matched filters Accuracy Sensitivity iterative closest point algorithm Filtering algorithms Information filters Linear programming Robustness iterative methods

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Chinese Automation Congress (CAC)

作者： Zenghui Xie Lin Wang Hao Fan College of Electric Power Inner Mongolia University of Technology Hohhot China College of Electric Power Shanghai Jiao Tong University Ningbo Artificial Intelligence Research Institute Ningbo China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

To enhance the scene understanding capability of inspection robots in dynamic environments, this paper proposes a system for calculating scene similarity based on point cloud data to comprehend the connections between two scenes. The point cloud data is acquired through ORB-SLAM2 and integrated with YOLOv5 to exclude dynamic interference, ensuring the precise capture of scene data. The similarity calculation system downsamples the input point cloud, uses the iterative closest point (ICP) algorithm for preprocessing and alignment of the point clouds, and adapts to variable observation conditions through point cloud enhancement techniques. Feature extraction employs self-attention mechanisms and Convolutional Neural Networks (CNNs) for dimensionality enhancement to capture the details of the points, and finally calculates the similarity of the feature vectors using random evaluation metrics. Experimental results show that in the TUM dataset and actual scene tests, the similarity of data collected multiple times in the same scene exceeds 90%, while the similarity in different scenes is below 70%.

关键词： point cloud compression YOLO Measurement iterative closest point algorithm Interference Inspection Feature extraction Vectors iterative methods Robots

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Registering Articulated Objects With Human-in-the-loop Corrections

Registering Articulated Objects With Human-in-the-loop Corre...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Hagenow, Michael Senft, Emmanuel Laske, Evan Hambuchen, Kimberly Fong, Terrence Radwin, Robert Gleicher, Michael Mutlu, Bilge Zinn, Michael Univ Wisconsin Dept Mech Engn Madison WI 53706 USA Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA NASA Johnson Space Ctr 2101 NASA Pkwy Houston TX 77058 USA NASA Ames Res Ctr Intelligent Robot Grp Mountain View CA 94035 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Remotely programming robots to execute tasks often relies on registering objects of interest in the robot's environment. Frequently, these tasks involve articulating objects such as opening or closing a valve. However, existing humanin-the-loop methods for registering objects do not consider articulations and the corresponding impact to the geometry of the object, which can cause the methods to fail. In this work, we present an approach where the registration system attempts to automatically determine the object model, pose, and articulation for user-selected points using nonlinear fitting and the iterative closest point algorithm. When the fitting is incorrect, the operator can iteratively intervene with corrections after which the system will refit the object. We present an implementation of our fitting procedure for one degree-of-freedom (DOF) objects with revolute joints and evaluate it with a user study that shows that it can improve user performance, in measures of time on task and task load, ease of use, and usefulness compared to a manual registration approach. We also present a situated example that integrates our method into an end-to-end system for articulating a remote valve.

关键词： iterative closest point algorithm Fitting Manuals Programming Valves Human in the loop Time measurement

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A Two-Stage point Cloud Registration Method for Knee Joint Replacement Navigation 3

A Two-Stage Point Cloud Registration Method for Knee Joint R...

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3rd World Robot Conference (WRC) / Symposium on Advanced Robotics and Automation (SARA)

作者： Wei Minhua Wang Chaoqun Zhou Haifeng Cheng Xiao Kuang Shaolong Sun Lining Soochow Univ Suzhou Jiangsu Peoples R China Soochow Univ Appl Technol Coll Suzhou Jiangsu Peoples R China Suzhou Key Lab Intelligent Med & Equipment Suzhou Jiangsu Peoples R China

ISBN: (纸本)9781665418232

To resolve the problem that the iterative closest point (ICP) algorithm commonly used in knee joint replacement surgery registration had low registration accuracy and was easy to fall into local optimal solutions. An improved registration method based on feature point clouds was proposed. First, the Harris-3D algorithm was used to extract the feature point cloud of the three-dimensional model of the knee joint. Optical tracking system (OTS) was used to collect the point cloud of the corresponding area of the sawbone. Then, the Sample Consensus Initial Alignment (SAC -IA) algorithm was used to perform coarse registration on two point clouds. The ICP algorithm was used to make the registration matrix converge to an optimal solution. The k-d tree was used to find neighboring points to accelerate the iterative process. Finally, the simulation of the registration method was taken to prove the feasibility of the method. The accuracy of the knee joint surgery navigation registration experiment was carried out with the knee joint femur model as the object. Result shows that the error of the proposed registration algorithm is 2.12mm, while the error of the ICP algorithm is 6.30mm, which verifies the effectiveness of this method.

关键词： Integrated optics Solid modeling Biomedical optical imaging Automation Navigation iterative closest point algorithm Surgery

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Robust Model-Predictive Control for Inductively Coupled Plasma Generation With a Semiphysical Simulation

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第4期68卷 3380-3389页

作者： Wang, Junjie Liu, Yanming Liu, Xiaotao Li, Xiaoping Zhang, Haojie Wang, Di Xidian Univ Sch Aerosp Sci & Technol Plasma Electromagnet Phys Lab Extreme Environm Key LabMinist Educ China Xian 710071 Peoples R China China Informat Technol Design & Consulting Inst C Beijing 100048 Peoples R China

In this article, a novel robust tuning approach based on model-predictive control (MPC) for an inductively coupled plasma (ICP) generation process is presented. Several key aspects related to this methodology are the modeling of the ICP generation process, the robust joint tuning (RJT) algorithm, and the semiphysical simulation of the MPC algorithm. The ICP generation process model is built by a system identification method based on steady and dynamic responses, followed by a general model-predictive controller. Then, an automatic RJT algorithm is developed for the model-predictive controller to quickly obtain the optimal weighting parameters. The proposed RJT algorithm can minimize the ICP generation control system's maximum total variations in the output with a specific dynamic performance requirement and model uncertainty, providing a tradeoff between the dynamic performance and robustness. Finally, a semiphysical simulation system based on dSPACE and a field-programmable gate array is designed to illustrate the effectiveness of the model-predictive controller for the ICP generation control process and the RJT algorithm. Experimental results indicate the validity and good performance of the proposed model-predictive controller and the RJT algorithm.

关键词： iterative closest point algorithm Plasmas Argon Process control Mathematical model Robustness Generators Inductively coupled plasma (ICP) model-predictive control (MPC) model uncertainty robust tuning semiphysical simulation

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