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A Path Correction Method Based on Global and Local Matching for Robotic Autonomous Systems

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2022年第1期104卷 1-12页

作者： Wang, Nianfeng Shi, Xiaodong Zhong, Kaifan Zhang, Xianmin Chen, Wei South China Univ Technol Sch Mech & Automot Engn Guangdong Key Lab Precis Equipment & Mfg Technol Guangzhou 510640 Peoples R China Shenzhen Polytech Shenzhen 518055 Peoples R China

Normally, small batch parts, especially by manual assembly, are difficult to be manufactured automatically(e.g., welded, polished, sprayed) by robots because of deviation from a pre-taught path, which results from shape error and clamping error. To correct the pre-taught path, visual sensors can be used to detect it and guide the robots. However, some of the paths cannot be continuously detected due to work space limitation, and therefore, only part of them can be detected. As a result, it is difficult to directly match the incompleted detected path to pre-taught path with location errors and shape errors, which cause residual deviation after correction. This paper proposes an efficient and robust path correction method, that can be applied to robotic autonomous systems. The main contribution of this work is adopting global matching to maintain the original shape of the pre-taught path, which deals with location errors, and the local matching relaxes this constraint and addressed the shape errors. Structured light vision is used to detect the deviation of path to improve the detecting accuracy and reduce detection data size simultaneously. And a path-matching method taking account of both global and local features is developed based on iterative closest point algorithm (ICP). The proposed method consists of four steps: path scanning, global matching, local matching and data updating. Several robotic welding experiments are applied to verify the proposed method performance.

关键词： Path correction Structured light vision iterative closest point algorithm

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A feasibility study towards traceable calibration of size and form of microspheres by stitching AFM images using ICP point-to-plane algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第5期34卷 055009-055009页

作者： Dai, Gaoliang Degenhardt, Johannes Hu, Xiukun Wolff, Helmut Tutsch, Rainer Manske, Eberhard Phys Tech Bundesanstalt Bundesallee 100 D-38116 Braunschweig Germany China Jiliang Univ Coll Mat & Chem Hangzhou 310018 Peoples R China Tech Univ Carolo Wilhelmina Braunschweig IPROM Schleinitzstr 20 D-38106 Braunschweig Germany Tech Univ Ilmenau Inst Proc Measurement & Sensor Technol D-98694 Ilmenau Germany

We present a new method for traceable calibration of size and form error of microspheres, which was realised by stitching a series of atomic force microscopic (AFM) images measured at different orientations of microspheres using the metrological large range AFM of the PTB. The stitching algorithm is achieved using an iterative closest point point-to-plane algorithm. As the AFM tip geometry is one of the most significant error sources for the developed method, it was traceably calibrated to a line width standard (type IVPS100-PTB), whose feature geometry was calibrated with a traceable route to the lattice constant of crystal silicon. Measurement setup, scan strategy, and data evaluation processes have been detailed in the paper. Measurement results show high stability and robustness of the developed method. For instance, the standard deviation of four repeated measurements reaches 5 nm, indicating promising performance.

关键词： microsphere calibration micro coordinate measuring machine atomic force microscopy iterative closest point algorithm data fusion

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Three-dimensional point cloud registration algorithm based on the correlation coefficient

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JOURNAL OF ELECTRONIC IMAGING 2023年第1期32卷 013010-013010页

作者： Zou, Xuyan He, Hanwu Li, Chaorong Guangdong Univ Technol Sch Electromech Engn Guangzhou Peoples R China Yibin Univ Dept Artificial Intelligence & Big Data Yibin Peoples R China

point cloud registration is an important research problem in machine vision. One of the most widely used methods for point set registration is the iterative closest point (ICP) algorithm. However, ICP is known to be susceptible to local minima because it is based on the local iterative optimization technique and its performance critically relies on the quality of the initialization. To reduce the possibility of the algorithm falling into local minima, we propose an iterative algorithm based on geometric distance and local feature weighting named weighted correlation coefficient iterative closest point algorithm (WCC_ICP). The algorithm first establishes the corresponding relationship of points using linear representation and constructs an optimization model with constraints. Subsequently, the constrained optimization model is converted into an unconstrained optimization model by Lagrangian multiplication. Finally, an iterative technique is used to solve the optimal rigid body transformation of the optimized model. In addition, in each iteration, the identification ability of the local feature operator of points is used to repair the corresponding relation of points, which reduces the risk of the iterative algorithm falling into the local minima. Numerical experiments show that the proposed WCC_ICP algorithm reduces the risk of the iterative algorithm falling into local minima.

关键词： point cloud registration correlation coefficient iterative closest point algorithm weighting function

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Foundations for Strip Adjustment of Airborne Laserscanning Data with Conformal Geometric Algebra

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ADVANCES IN APPLIED CLIFFORD ALGEBRAS 2022年第1期32卷 1-34页

作者： Hitzer, Eckhard Benger, Werner Niederwieser, Manfred Baran, Ramona Steinbacher, Frank Int Christian Univ Tokyo Japan Airborne HydroMapping GmbH Feldstr 1b A-6020 Innsbruck Austria Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

Typically, airborne laserscanning includes a laser mounted on an airplane or drone (its pulsed beam direction can scan in flight direction and perpendicular to it) an intertial positioning system of gyroscopes, and a global navigation satellite system. The data, relative orientation and relative distance of these three systems are combined in computing strips of ground surface point locations in an earth fixed coordinate system. Finally, all laserscanning strips are combined via iterative closest point methods to an interactive three-dimensional terrain map. In this work we describe the mathematical framework for how to use the iterative closest point method for the adjustment of the airborne laserscanning data strips in the framework of conformal geometric algebra.

关键词： Conformal geometric algebra Georeferencing iterative closest point algorithm Orientation Calibration

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An Improved point Cloud Registration Strategy based on Simulated Annealing and Barzilai-Borwein Gradient

An Improved Point Cloud Registration Strategy based on Simul...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Dingfei Jin Ying Li Fang Wu Peiru Wu School of Automation and Electronic Information University of Xiangtan Xiangtan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Three-dimensional (3D) point cloud registration is a fundamental problem in computer and robotic vision. The iterative closest point (ICP) is the most widely used registration method but it usually suffers from local minima. To address the local minima of ICP without increasing too much computational cost, we propose an improved ICP algorithm for fast global 3D point cloud registration based on simulated annealing (SA) and Barzilai-Borwein (BB) gradient. In this scenario, simulated annealing is adopted to solve the local minima problem of ICP, and BB gradient to improve the calculation efficiency. Furthermore, during the executive process of the algorithm, we have set flexible termination conditions for the loop to relieve its computational burden. Extensive experiments show that our method ensures competitive performance in precision to the typical similar registration algorithms, and its efficiency is improved evidently.

关键词： point cloud compression Three-dimensional displays Costs iterative closest point algorithm Simulated annealing Computational efficiency iterative methods Robots Optimization Convergence

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Enhancing Mesh and point Cloud Similarity Detection through Geometric Features and ICP

Enhancing Mesh and Point Cloud Similarity Detection through ...

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International Conference on Computer Science and Engineering (UBMK)

作者： Talha Rehman Abid Mehtap Öklü Cem Yildiz Ali Erman Erten Kamer Kaya Dakik Software Technologies Ankara Türkiye ERMETAL Bursa Türkiye Sabanci University Istanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

In this work, we propose a novel approach to assess the similarity between 3D data, aimed at enhancing the accuracy and robustness of 3D shape analysis. Our approach begins with the extraction of geometric features, including the dimensions of the oriented bounding box (OBB), Betti numbers indicating topological complexity, and variance metrics derived from surface normals and Fast point Feature Histograms (FPFH). These features capture both the geometric and topological essence of the shapes, facilitating a nuanced preliminary similarity assessment. Following this, we employ a refined iterative closest point (ICP) algorithm, enhanced with Random Sample Consensus (RANSAC), to meticulously align and compare the top 50 preliminary matched parts. This two-stage process leverages geometric feature normalization and Manhattan distances for initial part selection, before applying a PCA -oriented ICP for precise alignment. Our method addresses the common challenges in aligning parts with significant dimensional disparities, offering an improved framework for mesh and point cloud similarity detection. The experiments on a dataset containing 73 3D metal parts reveal that the proposed approach outputs the top choice of domain experts for 48 metal parts. Furthermore, when the top-5 similar parts are returned by the approach, in 66/73 of cases, the output contains the experts' choice.

关键词： point cloud compression Three-dimensional displays Accuracy Shape iterative closest point algorithm Metals Feature extraction Robustness iterative methods Principal component analysis

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point Cloud Registration Method for Power Transmission Lines based on Combined Feature points

Point Cloud Registration Method for Power Transmission Lines...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Ruitong Hu Wei Guo Wei Qian Xingfeng Mi Yang Liu Xu Shi Yiming Cao Sen Peng Zhenyu Xu Jingkai Zhu Nanjing NARI Information and Communication Technology Co. Ltd Nanjing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

In view of the problems that traditional point cloud registration algorithms are inefficient and prone to local optimality when processing large-scale power transmission line point cloud data, this paper proposes a point cloud registration method based on combined feature points (CFMPCR). First, the adaptive octree is used to downsample the point cloud, and then the combined feature points are extracted by combining 3D-SIFT and Alpha Shape algorithms, and the feature points are matched by FPFH. The SAC-IA algorithm is used for coarse registration, and the improved ICP algorithm is used for fine registration, that is, the iterative closest point algorithm from point to surface combined with KD-Tree for acceleration. Experimental results show that CFMPCR is significantly superior to the traditional ICP registration algorithm in both registration accuracy and efficiency. The registration accuracy is improved by about two orders of magnitude, and the registration time is reduced by 80%. The method CFMPCR proposed in this paper has good robustness and registration accuracy when processing large-scale point cloud data, and the registration effect is better than the existing algorithm.

关键词： point cloud compression Accuracy Power transmission lines Shape iterative closest point algorithm Octrees Feature extraction Data processing Robustness Surface treatment

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Research on the Application of TrICP-NDT Fusion Method in point cloud alignment

Research on the Application of TrICP-NDT Fusion Method in Po...

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Artificial Intelligence and Electromechanical Automation (AIEA), International Conference on

作者： Sen Wang Hanbing Yan Guoliang Zhang Wenyang Zhang Fei Luo Technology School of Automation chengdu Chengdu University of Information China Chengdu Southern Electronic Instrument Co. Ltd chengdu China

ISBN: (数字)9798350366174

ISBN: (纸本)9798350366181

The TrICP-NDT point cloud matching algorithm is proposed to address the problem of traditional point cloud matching algorithms easily falling into local optima when the initial position deviation is large or there is excessive point cloud noise. To begin with, by improving the ICP (iterative closest point) algorithm and adding iterative pruning parameters, only points with a distance greater than the threshold are retained during point cloud iteration. Outliers or unrelated point pairs in the point cloud are pruned to reduce the interference of point cloud noise points; Subsequently, combined with the NDT (Normal Distributions Transform) algorithm, coarse registration is adopted, and the NDT algorithm is used to provide a good initial relative position for registration; Ultimately, the TrICP (Trimmed iterative closest point)algorithm is used for precise registration to obtain the registered point cloud information. The experimental results show that the registration accuracy of the proposed TrICP-NDT algorithm is superior to similar registration algorithms, and it has good robustness.

关键词： point cloud compression iterative closest point algorithm Navigation Noise Transforms Interference Gaussian distribution Robustness Mobile robots Collision avoidance

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point Cloud Registration Network Based on Daul-Attention Mechanism For Truck Pose Estimation

Point Cloud Registration Network Based on Daul-Attention Mec...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Yi An Hongliang Xu Yunfei Guo Jianqiang Qiao Zhuo Sun Lirong Xie School of Control Science and Engineering Dalian University of Technology Dalian China School of Electrical Engineering Xinjiang University Urumqi China State Key Laboratory of Intelligent Mining Equipment Technology Taiyuan China Mining Equipment Subco Taiyuan Heavy Industry Co. LTD Taiyuan China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Truck pose estimation is a pivotal technology for automating excavation and loading processes in intelligent mining excavators. The existing methods for pose estimation are mainly based on sensor measurements and traditional registration techniques. While these sensor-based methods can identify truck poses, they are prone to significant cumulative errors due to factors like vibrations and also suffer from short sensor lifespans. Traditional registration methods use iterative closest point algorithms to estimate the bucket’s pose by aligning its point clouds, but these methods are prone to getting stuck in local optima. Recently, deep learning-based networks for object pose estimation have been introduced. However, in the mining operation environment, the truck’s position changes significantly during the loading process, leading to distinct truck features at different poses. Traditional point cloud registration networks often just concatenate the features of template and target point clouds, which inadequately exploits both global and local features. This limitation decreases the accuracy of truck pose estimation. To tackle the feature fusion challenge, we introduce a novel feature fusion registration network named CASNet. CASNet consists of a feature extraction module, a feature fusion module, and a pose computation module. The feature fusion module employs spatial and channel attention mechanisms to effectively learn different hierarchical features during feature fusion. By integrating both local and global features, CASNet addresses feature fusion issues and enhances generalization capabilities. Experimental results show that our method significantly reduces both rotational and translational errors in truck pose estimation by 1.26° and 2.74 cm, respectively.

关键词： point cloud compression Vibrations Attention mechanisms Accuracy iterative closest point algorithm Fuses Pose estimation

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Enhanced iteration closest point is based on stochastic differential evolution algorithm and random sampling

Enhanced iteration closest point is based on stochastic diff...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Shengwei Fu Ke Li Haisong Huang Yongpeng Zhao Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

To address the issue of low accuracy in 3D point cloud registration, we present a novel iterative closest point (ICP) algorithm for point registration using the stochastic differential evolution algorithm and random sampling (SDE_ICP). Given ICP's sensitivity to the initial transformation matrix, our approach divides the algorithm into two stages: coarse registration and fine registration. In the coarse registration phase, we introduce a random sampling algorithm to the original point cloud, expediting the coarse registration process. Subsequently, we propose a stochastic differential evolution (SDE) algorithm with random opposition-based learning population initialization, random centroid mutation, and a boundary control strategy. The search individual comprises translation and rotation transformation parameters, offering a robust relative initial position for subsequent ICP fine registration. Finally, we conduct simulations and analyses using Bimba, Angle, and Bunny datasets from FGR to evaluate SDE_ICP. We compare its performance with ICP, TrICP, DE_ICP, and SaDE_ICP. The results indicate that the proposed method exhibits superior accuracy.

关键词： point cloud compression Analytical models Three-dimensional displays Accuracy Sensitivity iterative closest point algorithm Sociology

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