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检索条件"主题词=Iterative Closest Point algorithm"

共 1673 条记录，以下是151-160 订阅

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Precise iterative closest point Algrithm Based on Correntropy for 3-D Oral Data Registration

Precise Iterative Closest Point Algrithm Based on Correntrop...

引用

Chinese Automation Congress (CAC)

作者： Yuying Liu Shaoyi Du Wenting Cui Teng Wan Qixing Xie Mengqi Han Guang Chu Yucheng Guo Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an Shaanxi Province P.R. China Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research College of Stomatology Xi’an Jiaotong University Xi’an Shaanxi Province P.R. China

This paper proposes a new iterative closest point approach based on correntropy with feature guided. iterative closest point (ICP) algorithm can deal with most rigid registration problems, but for point sets with lots of noise and outliers, ICP cannot achieve high precision. We introduce correntropy into ICP to handle this problem by suppressing the influence of the noise and outliers. In terms of point sets contain a large proportion of planes or a curved surface, and have single structure, such as a three-dimensional model of upper jaw, we propose a feature-guided model to solve the oral data registration problem, which uses both the feature and the original data to participate in the registration, but with different weights. Our method mainly deals with the point set registration which has single structure and contains outliers. Experimental results demonstrate that the proposed algorithm is precise and robust.

关键词： iterative closest point algorithm Periodic structures Three-dimensional displays Solid modeling Data models Teeth Euclidean distance

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PCAOT: A Manhattan point Cloud Registration Method towards Large Rotation and Small overlap

PCAOT: A Manhattan Point Cloud Registration Method towards L...

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25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Guo, Ping Hu, Wei Ren, Haibing Zhang, Yimin Intel Corp Intel Labs China Beijing 100190 Peoples R China

ISBN: (纸本)9781538680940

point cloud registration is a popular research topic and has been widely used in many tasks, such as robot mapping and localization. It is a challenging problem when the overlap is small, or the rotation is large. The problem has not been well solved by existing methods such as the iterative closest point (ICP) and its variants. In this paper, a novel method named principal coordinate alignment with overlap tuning (PCAOT) is proposed based on the Manhattan world assumption. It solves two key problems together, the transformation estimation and the overlap estimation. The overlap is represented by a 3D cuboid and the transformation is computed only within the overlap region. Instead of finding point correspondence as in traditional methods, we estimate the rotation by principal coordinates alignment, which is faster and less sensitive than ICP and its variants to small overlaps and large rotations. Evaluations demonstrate that our method achieves much better results than the ICP and its variants when the overlap ratio is smaller than 50%, or the rotation angle is larger than 60 degrees. Especially, it is effective when the overlap ratio is less than 30%, or the rotation angle is larger than 90 degrees.

关键词： Three-dimensional displays Estimation Tuning iterative closest point algorithm Mathematical model Filtering Task analysis

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Plane Loop Closure Based point Cloud Registration Using Structured Light Sensor

Plane Loop Closure Based Point Cloud Registration Using Stru...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Daoxiong Gong Jin Zhao Jing Xu Faculty of Information Technology Beijing University of Technology Beijing China State Key Laboratory of Tribology Tsinghua University Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

In the field of manufacturing such as additive manufacturing and rapid prototyping, the establishment of a 3D model of workpieces has great significance for the quality inspection, modeling, subsequent research, and application of the workpiece. However, there are still some problems in incompleteness and poor reduction for the complete 3D model of the workpiece, which limits the efficiency and accuracy of workpiece 3D quality inspection. In addition, due to the limitation of the field of view in the 3D measurement system, the generated local point cloud cannot completely describe the 3D information of the workpiece. To solve these problems, this paper proposes a point cloud registration method based on the structured light sensor, which combines loop closure optimization through the planar features in the local point clouds. This method considers the planar features to constrain the point clouds, and the point clouds were re-registered to improve the accuracy of point cloud registration. The feasibility and effect of the method are verified in the simulation and experiment, and the method can be applied to the fields of quality inspection.

关键词： Three-dimensional displays Feature extraction Cameras Solid modeling Optimization Inspection iterative closest point algorithm

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Position Measurement of the Hole Group Based on point Cloud Registration algorithm

Position Measurement of the Hole Group Based on Point Cloud ...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Dongyang Zhao Ming Li Qingyue Wei Yang Yang Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University China Shanghai China

ISBN: (数字)9781728146539

ISBN: (纸本)9781728146546

The position error of the hole group in a precision part directly affects the assembly quality. The accurate measurement of the position error for the hole group is the premise of judging whether the parts are qualified or not. In this paper, the central coordinates of the hole group are measured by the coordinate measuring machine, and the position of the hole group is evaluated based on the point cloud registration algorithm. The registration process is optimized for the possible problems of the experimental results. The experimental results show that the optimized point cloud registration algorithm is accurate and reliable, with high registration accuracy, and can accurately assess the position of the hole group.

关键词： Three-dimensional displays Position measurement Coordinate measuring machines Measurement uncertainty Covariance matrices Optimization iterative closest point algorithm

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Lossless Dynamic point Cloud Geometry Compression with Inter Compensation and Traveling Salesman Prediction

Lossless Dynamic Point Cloud Geometry Compression with Inter...

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Data Compression Conference (DCC)

作者： Kathariya, Birendra Li, Li Li, Zhu Alvarez, Jose R. Univ Missouri 5100 Rockhill Rd Kansas City MO 64110 USA Futurewei Technol Inc 2330 Cent Expressway Santa Clara CA 95050 USA

ISBN: (纸本)9781538648834

point cloud has shown great potential in many different applications including visual communication and auto-navigation systems. However, the immense size of the point cloud has been a bottleneck for its popularity in the consumer level. In this paper, we address the lossless compression of positional data of dynamic point cloud by viewing the point prediction of point cloud as Travelling Salesman Problem (TSP).

关键词： Three-dimensional displays Surface reconstruction Image coding Urban areas Binary trees iterative closest point algorithm Data compression

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DeepVCP: An End-to-End Deep Neural Network for point Cloud Registration

DeepVCP: An End-to-End Deep Neural Network for Point Cloud R...

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International Conference on Computer Vision (ICCV)

作者： Weixin Lu Guowei Wan Yao Zhou Xiangyu Fu Pengfei Yuan Shiyu Song Baidu Autonomous Driving Technology Department (ADT)

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

We present DeepVCP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the interference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable registration accuracy and runtime efficiency to the state-of-the-art geometry-based methods, but with higher robustness to inaccurate initial poses. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method make it attractive to the substantial applications relying on the point cloud registration task.

关键词： Three-dimensional displays Feature extraction Task analysis Laser radar Iron Estimation iterative closest point algorithm

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Pose Estimation with Dual Quaternions and iterative closest point

Pose Estimation with Dual Quaternions and Iterative Closest ...

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American Control Conference

作者： Sveier, Aksel Myhre, Torstein A. Egeland, Olav Norwegian Univ Sci & Technol NTNU Dept Mech & Ind Engn N-7491 Trondheim Norway

ISBN: (纸本)9781538654286

This paper presents a method for pose estimation of a rigid body using unit dual quaternions where pose measurements from point clouds are filtered with a multiplicative extended Kalman filter (MEKF). The point clouds come from a 3D camera fixed to the moving rigid body, and then consecutive point clouds are aligned with the iterative closest point (ICP) algorithm to obtain pose measurements. The unit constraint of the dual quaternion is ensured in the filtering process with the Dual Quaternion MEKF (DQ-MEKF), where the measurement updates are performed using the dual quaternion product so that the result is a unit dual quaternion. In addition, we use the Cayley transform for the discrete time propagation of the DQ-MEKF estimate, eliminating the need for normalization and projection of the resulting unit dual quaternion. The ICP algorithm is initialized with the time propagated state of the filter to give faster and more accurate pose measurements. To further improve the accuracy of the initialization, angular velocity measurements from a gyroscope fixed to the camera are included in the filter. The proposed method has been tested in experiments using a Kinect v2 3D camera mounted rigidly on a KUKA KR6 robotic arm, while a customized ICP algorithm was successfully implemented on a Graphical Processing Unit (GPU) system.

关键词： Quaternions Three-dimensional displays iterative closest point algorithm Cameras Kalman filters Transforms Pose estimation"

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Rigid Registration of point Clouds Based on Indirect Lie Group Approach

Rigid Registration of Point Clouds Based on Indirect Lie Gro...

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Symposium on Virtual Reality (SVR)

作者： Liliane Rodrigues de Almeida Gilson Antonio Giraldi Marcelo Bernardes Vieira COMAC Petropolis Brazil COMAC Lab. Nacional de Computação Científica Petrópolis RJ Brasil Universidade Federal de Juiz de Fora Juiz de Fora Brazil

The following topics are dealt with: virtual reality; augmented reality; mobile computing; computer aided instruction; user interfaces; patient rehabilitation; medical computing; human computer interaction; image colo... 详细信息

关键词： Tensile stress Three-dimensional displays Algebra iterative closest point algorithm Geometry Shape Augmented reality

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Structured Down-Sampling and Registration Method for 3D point Cloud of Indoor Scene

Structured Down-Sampling and Registration Method for 3D Poin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yang Yang Haozhe Li Jing Yang Dexing Zhong School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Shenzhen Research School Xi’an Jiaotong University Shenzhen China

In this paper, noted by the regular geometric structure of indoor scene, a biased down-sampling scheme is designed to automatically adjust the local sampling rate according to the local density and distribution. Our down-sampling results can effectively remain the main structure while greatly reduce the data number for the following work. Moreover, an improved iterative closest point (ICP) algorithm for point clouds registration is proposed with the prior of structure information. Sampled structured data is weighted to give their contributions for registration. This leads the parameter estimation to naturally focus on aligning the structures of indoor scenes. The experimental results demonstrate the effectiveness of the proposed method on improving the registration accuracy with the same level of down-sampling data.

关键词： Three-dimensional displays Principal component analysis Eigenvalues and eigenfunctions Sensors Feature extraction iterative closest point algorithm Sampling methods

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EOE: Expected Overlap Estimation over Unstructured point Cloud Data 6

EOE: Expected Overlap Estimation over Unstructured Point Clo...

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6th International Conference on 3D Vision (3DV)

作者： Eckart, Benjamin Kim, Kihwan Kautz, Jan NVIDIA Res Santa Clara CA 95051 USA

ISBN: (纸本)9781538684252

We present an iterative overlap estimation technique to augment existing point cloud registration algorithms that can achieve high performance in difficult real-world situations where large pose displacement and non-overlapping geometry would otherwise cause traditional methods to fail. Our approach estimates overlapping regions through an iterative Expectation Maximization procedure that encodes the sensor field-of-view into the registration process. The proposed technique, Expected Overlap Estimation (EOE), is derived from the observation that differences in field-of-view violate the iid assumption implicitly held by all maximum likelihood based registration techniques. We demonstrate how our approach can augment many popular registration methods with minimal computational overhead. Through experimentation on both synthetic and real-world datasets, we find that adding an explicit overlap estimation step can aid robust outlier handling and increase the accuracy of both ICP-based and GMM-based registration methods, especially in large unstructured domains and where the amount of overlap between point clouds is very small.

关键词： Three-dimensional displays Estimation iterative closest point algorithm Geometry Data models Two dimensional displays

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