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

共 1671 条记录，以下是81-90 订阅

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point Cloud Registration with Virtual Interest points from Implicit Quadric Surface Intersections 7

Point Cloud Registration with Virtual Interest Points from I...

引用

International Conference on 3D Vision (3DV)

作者： Ahmed, Mirza Tahir Marshall, Joshua A. Greenspan, Michael Queens Univ Dept Elect & Comp Engn Kingston ON Canada

ISBN: (纸本)9781538626108

A novel method is presented to robustly and efficiently register two partially overlapping point clouds. Following segmentation, the regions are represented as implicit quadric surfaces using polynomials of degree two in three variables. The registration establishes correspondences of virtual interest points, which do not exist in the original point cloud data, and are defined by the intersection of three implicit quadric surfaces extracted from the point cloud regions. Implicit quadric surfaces exist in abundance in both natural and architectural scenes, and can be used to identify stable regions in the data, which in turn leads to repeatable virtual interest points. Large regions in a point cloud can be represented by a few implicit surfaces, which reduces the computational cost of registration and also makes the algorithm robust to noise and data density variations. Experiments were performed on seven data sets from various sensors. The proposed method outperformed most of the feature based registration and non-feature based registration methods for computational efficiency and convergence.

关键词： Three-dimensional displays Feature extraction Sensors Shape Registers Data mining iterative closest point algorithm

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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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Local-to-Global point Cloud Registration using a Dictionary of Viewpoint Descriptors 16

Local-to-Global Point Cloud Registration using a Dictionary ...

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16th IEEE International Conference on Computer Vision (ICCV)

作者： Avidar, David Malah, David Barzohar, Meir Technion Dept Elect Engn IL-32000 Haifa Israel

ISBN: (纸本)9781538610329

Local-to global point cloud registration is a challenging task due to the substantial differences between these two types of data, and the different techniques used to acquire them. Global clouds cover large-scale environments and are usually acquired aerially, e.g., 3D modeling of a city using Airborne Laser Scanning (ALS). In contrast, local clouds are often acquired from ground level and at a much smaller range, for example, using Terrestrial Laser Scanning (TLS). The differences are often manifested in point density distribution, occlusions nature, and measurement noise. As a result of these differences, existing point cloud registration approaches, such as keypoint-based registration, tend to fail. We improve upon a different approach, recently proposed, based on converting the global cloud into a viewpoint-based cloud dictionary. We propose a local-to-global registration method where we replace the dictionary clouds with viewpoint descriptors, consisting of panoramic range-images. We then use an efficient dictionary search in the Discrete Fourier Transform (DFT) domain, using phase correlation, to rapidly find plausible transformations from the local to the global reference frame. We demonstrate our method's significant advantages over the previous cloud dictionary approach, in terms of computational efficiency and memory requirements. In addition, We show its superior registration performance in comparison to a state-of-the-art, keypoint-based method (FPFH). For the evaluation, we use a challenging dataset of TLS local clouds and an ALS large-scale global cloud, in an urban environment.

关键词： Dictionaries Three-dimensional displays iterative closest point algorithm Urban areas Laser noise Measurement by laser beam Memory management

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Inclusion of Angular Momentum During Planning for Capture point Based Walking

Inclusion of Angular Momentum During Planning for Capture Po...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Seyde, Tim Shrivastava, Apoorv Englsberger, Johannes Bertrand, Sylvain Pratt, Jerry Griffin, Robert J. Florida Inst Human & Machine Cognit IHMC 40 S Alcaniz St Pensacola FL 32502 USA German Aerosp Ctr DLR Inst Robot & Mechatron D-82234 Wessling Germany

ISBN: (纸本)9781538630815

When walking at high speeds, the swing legs of robots produce a non-negligible angular momentum rate. To accommodate this, we provide a reference trajectory generator for bipedal walking that incorporates predicted centroidal angular momentum at the planning stage. This can be done efficiently as the Centroidal Moment Pivot (CMP), Instantaneous Capture point (ICP) and the center of mass (CoM) all have closed-form trajectory solutions due to their linear dynamics. This is then used to produce smooth, continuous trajectories. We furthermore provide a lightweight model to estimate angular momentum as induced during leg swing of the gait cycle. Our proposed trajectory generator is tested thoroughly in simulation and has been shown to successfully operate on the real hardware.

关键词： Legged locomotion Trajectory iterative closest point algorithm Planning Dynamics Lips"

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Inverse Composition Discriminative Optimization for point Cloud Registration 31

Inverse Composition Discriminative Optimization for Point Cl...

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31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Vongkulbhisal, Jayakorn Ugalde, Benat Irastorza De la Torre, Fernando Costeira, Joao P. Univ Lisbon ISR IST Lisbon Portugal Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook Inc Menlo Pk CA USA

ISBN: (纸本)9781538664209

Rigid point Cloud Registration (PCReg) refers to the problem of finding the rigid transformation between two sets of point clouds. This problem is particularly important due to the advances in new 3D sensing hardware, and it is challenging because neither the correspondence nor the transformation parameters are known. Traditional local PCReg methods (e.g., ICP) rely on local optimization algorithms, which can get trapped in bad local minima in the presence of noise, outliers, bad initializations, etc. To alleviate these issues, this paper proposes Inverse Composition Discriminative Optimization (ICDO), an extension of Discriminative Optimization (DO), which learns a sequence of update steps from synthetic training data that search the parameter space for an improved solution. Unlike DO, ICDO is object-independent and generalizes even to unseen shapes. We evaluated ICDO on both synthetic and real data, and show that ICDO can match the speed and outperform the accuracy of state-of-the-art PCReg algorithms.

关键词： Three-dimensional displays Shape Optimization Integrated circuits Training data Annealing iterative closest point algorithm

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point Cloud Registration using Congruent Pyramids

Point Cloud Registration using Congruent Pyramids

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

作者： Krishnan, Aravindhan K. Saripalli, Srikanth Arizona State Univ Sch Earth & Space Explorat Tempe AZ 85287 USA

ISBN: (纸本)9781479969340

present a method to compute an initial alignment for pairwise registration of point clouds. This method uses the properties of a rigid body transformation-the ratio of lengths is preserved, the euclidean distance between points is preserved-to find congruent pyramids in two point clouds. The corresponding vertices of the congruent pyramids are used to derive a closed form solution for initial alignment. The alignment is refined further using the iterative closest point algorithm. We validate the method on challenging datasets which include airborne LIDAR, outdoor, and indoor-having initial offsets and varying densities.

关键词： Closed-form solutions Entropy Equations iterative closest point algorithm Laser radar Surfaces Three-dimensional displays

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Color point Cloud Registration with 4D ICP algorithm

Color Point Cloud Registration with 4D ICP Algorithm

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Men, Hao Gebre, Biruk Pochiraju, Kishore Stevens Inst Technol Dept Mech Engn Hoboken NJ 07030 USA

ISBN: (纸本)9781612843858

This paper presents methodologies to accelerate the registration of 3D point cloud segments by using hue data from the associated imagery. The proposed variant of the iterative closest point (ICP) algorithm combines both normalized point range data and weighted hue value calculated from RGB data of an image registered 3D point cloud. A k-d tree based nearest neighbor search is used to associated common points in {x, y, z, hue} 4D space. The unknown rigid translation and rotation matrix required for registration is iteratively solved using Singular Value Decomposition (SVD) method. A mobile robot mounted scanner was used to generate color point cloud segments over a large area. The 4D ICP registration has been compared with typical 3D ICP and numerical results on the generated map segments shows that the 4D method resolves ambiguity in registration and converges faster than the 3D ICP.

关键词： Cameras Computational modeling Data models Image color analysis iterative closest point algorithm Laser radar Three dimensional displays

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NRGA Gravitational Approach for Non-Rigid point Set Registration 6

NRGA Gravitational Approach for Non-Rigid Point Set Registra...

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

作者： Ali, Sk Aziz Golyanik, Vladislav Stricker, Didier Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI GmbH Kaiserslautern Germany

ISBN: (纸本)9781538684252

Recovery of correspondences between point sets which differ by some non-rigid transformation is an ill-posed problem. Many existing methods underperform on noisy or corrupted input data. In this study, a novel physics-based approach - Non-Rigid Gravitational Approach (NRGA) - for non-rigid point set registration is introduced which is robust to the mentioned artifacts. Thereafter, a distributed N-body simulation and iterative Procrustes alignment nonrigidly transform and register the template point set. Furthermore, in the force field evolution, per-point Gaussian curvature serves as a shape matching descriptor whereas the displacement fields are regularized by coherent collective motion. The optimal alignment is referred to as the state of minimum gravitational potential energy between the point sets. A thorough experimental evaluation and comparison are provided with widely used state-of-the-art methods on 2D and 3D datasets. Experiments show NRGA's robustness against uniform outliers and missing data.

关键词： Gravity Shape Three-dimensional displays iterative closest point algorithm Noise measurement Transforms

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Shape Reconstruction from Multiple RGB-D point Cloud Registration 12

Shape Reconstruction from Multiple RGB-D Point Cloud Registr...

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12th IEEE International Conference on Industrial Informatics (INDIN)

作者： Takimoto, Rogerio Y. Tsuzuki, Marcos S. G. Vogelaar, Renato Martins, Thiago C. Iwao, Yuma Gotoh, Toshiyuki Kagei, Seiichiro Gallo, Giulliano B. Garcia, Marcos A. A. Tiba, Hamilton Univ Sao Paulo Computat Geometry Lab Escola Politecn BR-09500900 Sao Paulo Brazil Yokohama Natl Univ Kanagawa Japan Companhia Energet Estado Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9781479949052

The objective of this work is to present an object 3D reconstruction method using the point color information. The object 3D reconstruction is performed by combining point clouds obtained from different viewpoints using two cameras and a structured light projector. The main task is the point cloud registration algorithm that matches two point clouds. A well known algorithm for point cloud registration is the ICP (iterative closest point) that determines the rotation and translation that when applied to one of the point clouds, place both point clouds in accordance. The ICP algorithm executes iteratively two main steps: point correspondence determination and registration algorithm. The point correspondence determination is a module that if not executed properly can make the ICP to converge to a local minimum. To overcome such drawback an ICP that uses statistics to generate a dynamic distance and color threshold on the distance allowed between closest points is proposed and implemented. This approach allows subset matches, instead of matching all points from the point clouds. The surface reconstruction is performed using Marching Cubes and a consensus surface algorithm with signed distance compensates point cloud errors. In this paper the performance of the proposed method is analyzed and compared with the classical ICP.

关键词： Computational modeling Image color analysis Image reconstruction iterative closest point algorithm Sensors Surface reconstruction Three-dimensional displays

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Efficient and Accurate Registration of point Clouds with Plane to Plane Correspondences 16

Efficient and Accurate Registration of Point Clouds with Pla...

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16th IEEE International Conference on Computer Vision (ICCV)

作者： Foerstner, Wolfgang Khoshelham, Kourosh Univ Bonn Inst Geodesy & Geoinformat Bonn Germany Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781538610343

We propose and analyse methods to efficiently register point clouds based on plane correspondences. This is relevant in man-made environments, where most objects are bounded by planar surfaces. Based on a segmentation of the point clouds into planar regions and matches of planes in different point clouds, we (1) optimally estimate the relative pose(s);(2) provide three direct solutions, of which two take the uncertainty of the given planes into account;and (3) analyse the loss in accuracy of the direct solutions as compared to the optimal solution. The paper presents the different solutions, derives their uncertainty especially of the suboptimal direct solutions, and compares their accuracy based on simulated and real data. We show that the direct methods that exploit the uncertainty of the planes lead to a maximum loss of 2.76 in accuracy of the estimated motion parameters in terms of the achieved standard deviations compared to the optimal estimates. We also show that the results are more accurate than the classical iterative closest point and iterative closest plane method, but the estimation procedures have a significantly lower computational complexity. We finally show how to generalize the estimation scheme to simultaneously register multiple point clouds.

关键词： Three-dimensional displays Uncertainty iterative closest point algorithm Motion segmentation Maximum likelihood estimation Motion estimation

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