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Triangular Model Registration algorithm Through Differential Topological Singularity points by Helmholtz-Hodge Decomposition

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IEEE ACCESS 2019年 7卷 34776-34790页

作者： Wu, Dongqing Gao, Jian Xiao, Zhengtao Zhang, Lanyu Chen, Xin Tang, Hui Chen, Yun He, Yunbo Guangdong Univ Technol State Key Lab Precis Elect Mfg Technol & Equipmen Guangzhou 510006 Guangdong Peoples R China Zhongkai Univ Agr & Engn Sch Computat Sci Guangzhou 510220 Guangdong Peoples R China Guangdong Polytech Ind & Commerce Sch Electromech Engn Guangzhou 510510 Guangdong Peoples R China

iterative closest point algorithms suffer from non-convergence and local minima when dealing with cloud points with a different sampling density. Alternative global or semi-global registration algorithms may suffer from efficiency problem. This paper proposes a new registration algorithm through the differential topological singularity points (DTSP) based on the Helmholtz-Hodge decomposition (HHD), which is called DTSP-ICP method. The DTSP-ICP method contains two algorithms. First, the curvature gradient fields on surfaces are decomposed by the HHD into three orthogonal parts: divergence-free vector field, curl-free vector field, and a harmonic vector field, and then the DTSP algorithm is used to extract the differential topological singularity points in the curl-free vector field. Second, the ICP algorithm is utilized to register the singularity points into one aligned model. The singularity points represent the feature of the whole model, and the DTSP algorithm is designed to capture the nature of the differential topological structure of a mesh model. Through the singularity alignment, the DTSP-ICP method, therefore, possesses better performance in triangular model registration. The experimental results show that independent of sampling schemes, the proposed DTSP-ICP method can maintain convergence and robustness in cases where other alignment algorithms including the ICP alone are unstable. Moreover, this DTSP-ICP method can avoid the local errors of model registration based on Euclidean distance and overcome the computation insufficiencies observed in other global or semi-global registration publications. Finally, we demonstrate the significance of the DTSP-ICP algorithm's advantages on a variety of challenging models through result comparison with that of two other typical methods.

关键词： iterative closest point algorithm computer aided manufacturing differential equations HHD

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ICP and IC3 with Stronger Generalization

ICP and IC3 with Stronger Generalization

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MBMV 2021;24th Workshop

作者： Felix Winterer Tobias Seufert Karsten Scheibler Tino Teige Chritsoph Scholl Bernd Becker

ISBN: (纸本)9783800755004

Most recently, IC3 was integrated into the SMT solver iSAT3. Thus, iSAT3+IC3 introduces the first IC3 variant based on interval abstraction and Interval Constraint Propagation (ICP). As strong generalization is one of the key aspects for the IC3 algorithm to be successful, we integrate two additional generalization schemes from literature into iSAT3+IC3: Inductive Generalization and Counterexamples To Generalization (CTG). Furthermore, we evaluate the benefits and the drawbacks of different variants of these methods in the context of interval abstraction and ICP.

关键词： iterative closest point algorithm Inductively coupled plasma Generalization Counter example Abstraction

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A method of seam recognization based on simulated laser stripe and ICP

A method of seam recognization based on simulated laser stri...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Nianfeng Wang Yu Fu Xianmin Zhang Xuewei Zheng Guangdong Province Key Laboratory of Precision Equipment and Manufacturing Technology South China University of Technology Guangzhou PR China Uppsala University Faculty of Science and Technology Uppsala Sweden

ISBN: (纸本)9781665431545

Recent years have seen the widespread use of welding robots equipped with a structured light vision sensing system. The extraction of features is crucial for the robot to weld precisely and efficiently. In practical application, however, interference inevitably exists, such as noises and reflection. Common noises include arc, spatter and smoke. Besides, many laser stripes and a flare around the seam would occur due to reflection. Interference would always have a negative effect on the recognization of feature points under these working conditions. A strategy based on the iterative closest point algorithm (ICP) and simulation is presented to recognize the target stripe and the seam position with interference. The method consists of three steps. Firstly, register the simulated stripe to get the target point set. Secondly, calculate the source point set at the position far away from the seam position. Finally, register these two point sets to extract the target stripe and recognize the seam position. Experimental results show that the method is capable of recognizing the seam position successfully under interference.

关键词： Target recognition Shape iterative closest point algorithm Laser noise Welding Machine vision Interference

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LOL: Lidar-only Odometry and Localization in 3D point cloud maps*

LOL: Lidar-only Odometry and Localization in 3D point cloud ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dávid Rozenberszki András L. Majdik Machine Perception Research Laboratory MTA SZTAKI Hungarian Academy of Sciences Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidaronly odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages. Also, we propose additional enhancements in order to reduce the number of false matches between the online point cloud and the target map, and to refine the position estimation error whenever a good match is detected. We demonstrate the utility of the proposed LOL system on several Kitti datasets of different lengths and environments, where the relocalization accuracy and the precision of the vehicle's trajectory were significantly improved in every case, while still being able to maintain real-time performance.

关键词： Three-dimensional displays Laser radar Sensors iterative closest point algorithm Image color analysis Trajectory Real-time systems

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A Fusion of CNNs and ICP for 3-D point Cloud Registration*

A Fusion of CNNs and ICP for 3-D Point Cloud Registration*

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Wen-Chung Chang Van-Toan Pham Yang-Cheng Huang Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

3-D point cloud registration appears to be one of the principal techniques to estimate object pose in 3-D space and is critical to object picking and assembly in automated manufacturing lines. Thereby, this paper proposes an effective registration architecture with the aim of estimating the transformation between a data point cloud and the model point cloud. Specifically, in the first registration stage, a trainable Convolutional Neural Network (CNN) model is developed to learn the pose estimation between two point clouds in the case of a full range of orientation from -180° to 180°. In order to generate the training data set, a descriptor is proposed to extract features which are employed to train the CNN model from point clouds. Then, based on the rough estimation of the trained CNN model in the first stage, two point clouds can be further aligned precisely in the second stage by using the iterative closest point (ICP) algorithm. Finally, the performance of the proposed two-stage registration architecture has been verified by experiments in comparison with a baseline method. The experimental results illustrate that the developed algorithm can guarantee high precision while significantly reducing the estimation time.

关键词： Three-dimensional displays Feature extraction iterative closest point algorithm Data models Estimation Convolution Training data

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TWOLATE: Total Registration of point-Clouds Using a Weighted Optimal Linear Attitude and Translation Estimator

TWOLATE: Total Registration of Point-Clouds Using a Weighted...

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Control Technology and Applications (CCTA),

作者： Duowen Qian Guillaume Charland-Arcand James Richard Forbes ARA Robotique Montreal QC Canada McGill University Montreal QC Canada

ISBN: (数字)9781728171401

ISBN: (纸本)9781728171418

A novel point-cloud registration method that can register points to points, points to planes, and points to lines simultaneously, and in a weighted fashion, is presented. The backbone of the proposed point-cloud registration method is a weighted version of the Optimal Linear Attitude and Translation Estimator (OLATE) called weighted OLATE (WOLATE). The first contribution of this work is the derivation of WOLATE versions of point-to-point, point-to-plane, and point-to-line, all of which take the form of weighted linear least-squares problems. This allows each WOLATE variant to be combined into one large weighted linear least-squares problem. Formulation of this combined registration algorithm, called Total Registration using WOLATE (TWOLATE), is the second contribution of this work. TWOLATE is embedded into a LIDAR odometry pipeline, and is shown to have similar performance relative to state-of-the-art methods, the third contribution of this work.

关键词： Cost function Measurement Robots iterative closest point algorithm Laser radar Pipelines Electronic mail

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3DRegNet: A Deep Neural Network for 3D point Registration

3DRegNet: A Deep Neural Network for 3D Point Registration

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： G. Dias Pais Srikumar Ramalingam Venu Madhav Govindu Jacinto C. Nascimento Rama Chellappa Pedro Miraldo Instituto Superior Técnico Lisboa Google Research NY Indian Institute of Science Bengaluru University of Maryland College Park

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results.

关键词： Three-dimensional displays Feature extraction Neural networks Machine learning iterative closest point algorithm Computer architecture Noise measurement

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Novel invariant feature descriptor and a pipeline for range image registration in robotic welding applications

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IET IMAGE PROCESSING 2019年第6期13卷 964-974页

作者： Idrobo-Pizo, Gerardo A. Motta, Jose Mauricio S. T. Borges, Dibio L. Univ Brasilia Dept Elect Engn Brasilia DF Brazil Univ Brasilia Dept Mech & Mechatron Engn Brasilia DF Brazil Univ Brasilia Dept Comp Sci CIC Brasilia DF Brazil

This work proposes an invariant descriptor and a pipeline for the registration of surface range images based on segmentation/reconstruction making use of an edge detection technique combined with a clustering technique using mesh decimation. This novel descriptor is applied to contours and it is invariant to similarity transformations including rotation, translation, uniform scale and it is robust to noise. The proposed feature descriptor makes use of corresponding points extracted from two images and a signature label is assigned specifically to a point considering the geometrical distribution of its neighbourhood, reducing possible areas of overlapping and the ambiguity in the search process. The descriptor was evaluated through a series of tests with various object range images. To validate the candidate transformations, the fitting errors between the two range images are evaluated by the iterative closest point algorithm. This study also presents and discusses results from the application of the developed pipeline in a vision sensor mounted on a robot arm specially built as part of a R&D project to acquire range images by laser scanning over the surface of hydraulic turbine blades. The sensor generates 3D surface models to be registered in the 3D coordinate system of the robot controller.

关键词： image registration solid modelling robotic welding image sensors blades edge detection robot vision object recognition hydraulic turbines image representation image reconstruction iterative methods computer vision image matching feature extraction image segmentation range image registration robotic welding applications surface range images edge detection technique clustering technique mesh decimation uniform scale object range images candidate transformations iterative closest point algorithm robot arm image reconstruction similarity transformations pipeline image segmentation invariant feature descriptor search process vision sensor hydraulic turbine blades 3D surface models 3D coordinate system robot controller

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Parallel 3D ICP Based on Conditionally Constrained Corresponding points and Applications

Parallel 3D ICP Based on Conditionally Constrained Correspon...

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IEEE International Conference on Networking, Sensing and Control

作者： Tun Dong Liu Fan Zhen Kong Miao He Xiao Ming Wu Gui Fang Shao Xiamen University Xiamen China

ISBN: (纸本)9781665440493

The iterative closest point (ICP) tends to fall into local optimality due to inaccurate initial poses during the registration of the multi-view 3D cloud. Therefore, this paper proposes a parallel ICP algorithm with conditional constraint corresponding points. To achieve a fine registration of the point cloud, the corresponding point set is first filtered by adding the normal and color information. Then the OpenMP is introduced to accelerate the program in parallel for ICP. To verify the effectiveness of our algorithm, in the V-Rep simulation environment, the multi-view point cloud data of the scene is obtained by the RGB-D cameras from different angles point cloud. The results show that our algorithm can fuse the multi-view point cloud, improve the accuracy and real-time performance of ICP. Furthermore, in a large-scale calculation, the average single iteration time is less than 0.1s, and the RMSE (root mean square error) is about 0.1, which meets the need of target recognition and sorting in a three-dimensional industrial scene.

关键词： point cloud compression Three-dimensional displays Target recognition iterative closest point algorithm Image color analysis Information filters Real-time systems

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Quantifying the pH shift induced by selective anodic electrochemical reactions in the ion concentration polarization phenomenon

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LAB ON A CHIP 2019年第8期19卷 1359-1369页

作者： Kim, Wonseok Oh, Jeewhan Kwon, Soonhyun Kim, Kihong Kim, Sung Jae Seoul Natl Univ Dept Elect & Comp Engn Seoul 08826 South Korea Univ Calif Berkeley Dept Bioengn Berkeley CA 94720 USA Univ Calif Berkeley Dept Berkeley Sensor & Actuator Ctr Berkeley CA 94720 USA Seoul Natl Univ Interuniv Semicond Res Ctr Seoul 08826 South Korea Seoul Natl Univ Nano Syst Inst Seoul 08826 South Korea

Recently, the ion concentration polarization (ICP) phenomenon has been actively utilized for low abundance biomolecular preconcentration applications. Since ICP significantly rearranges the ion distribution near a permselective membrane, its detailed investigation should be conducted for developing efficient platforms. In particular, proton transport through the membrane critically affects the pH of sample solutions so that continuous monitoring or batch measurement of pH is the priority task to be carried out. Moreover, electrochemical reactions have been overlooked, even though an overpotential is applied to preconcentrate a sample under physiological conditions, and the electrodes are in direct contact with the sample biomolecules. In this work, we experimentally visualized and directly measured how the electrochemical reaction dominated the preconcentration efficiency using two types of electrode configurations;large exposed electrode area (LEEA) and small exposed electrode area (SEEA). Interestingly, significant pH variation was confirmed only in the case of SEEA. As a result, the BSA preconcentration was impeded within a short period in the case of SEEA, but loss-free preconcentration was achieved in the case of LEEA. Therefore, one should pay careful attention to the electrode design of electrokinetic operation, especially when pH-sensitive biomolecules are involved.

关键词： Preconcentration ion concentration polarization phenomenon ion distribution design of electrode ELECTROCHEMICAL REACTIONS Permselective membranes proton transport Biomolecules Electrodes iterative closest point algorithm PH Inductively coupled plasma Continuous monitoring Polarization

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