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

共 1671 条记录，以下是71-80 订阅

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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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SectionKey: 3-D Semantic point Cloud Descriptor for Place Recognition

SectionKey: 3-D Semantic Point Cloud Descriptor for Place Re...

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

作者： Jin, Shutong Wu, Zhenyu Zhao, Chunyang Zhang, Jun Peng, Guohao Wang, Danwei Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Place recognition is seen as a crucial factor to correct cumulative errors in Simultaneous Localization and Mapping (SLAM) applications. Most existing studies focus on visual place recognition, which is inherently sensitive to environmental changes such as illumination, weather and seasons. Considering these facts, more recent attention has been attracted to use 3-D Light Detection and Ranging (LiDAR) scans for place recognition, which demonstrates more credibility by exerting accurate geometric information. Different from pure geometric-based studies, this paper proposes a novel global descriptor, named SectionKey, which leverages both semantic and geometric information to tackle the problem of place recognition in large-scale urban environments. The proposed descriptor is robust and invariant to viewpoint changes. Specifically, the encoded three-layers key serves as a pre-selection step and a `candidate center' selection strategy is deployed before calculating the similarity score, thus improving the accuracy and efficiency significantly. Then, a two-step semantic iterative closest point (ICP) algorithm is applied to acquire the 3-D pose ( x, y,.) that is used to align the candidate point clouds with the query frame and calculate the similarity score. Extensive experiments have been conducted on public Semantic KITTI dataset to demonstrate the superior performance of our proposed system over state-of-the-art baselines.

关键词： point cloud compression Visualization Simultaneous localization and mapping Laser radar iterative closest point algorithm Semantics Urban areas

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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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The Application of iterative closest point (ICP) Registration to Improve 3D Terrain Mapping Estimates Using the FLASH 3D LADAR System

The Application of Iterative Closest Point (ICP) Registratio...

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Conference on Laser Radar Technology and Applications XV

作者： Woods, Jack Armstrong, Ernest E. Armbruster, Walter Richmond, Richard AFRL RYJM Wright Patterson AFB OH 45433 USA Res Inst Optron & Pattern Recognit FGAN FOM D-76275 Ettlingen Germany ITT Informat Sys Beavercreek OH 45431 USA

ISBN: (纸本)9780819481481

The primary purpose of this research was to develop an effective means of creating a 3D terrain map image (point-cloud) in GPS denied regions from a sequence of co-bore sighted visible and 3D LIDAR images. Both the visible and 3D LADAR cameras were hard mounted to a vehicle. The vehicle was then driven around the streets of an abandoned village used as a training facility by the German Army and imagery was collected. The visible and 3D LADAR images were then fused and 3D registration performed using a variation of the iterative closest point (ICP) algorithm. The ICP algorithm is widely used for various spatial and geometric alignment of 3D imagery producing a set of rotation and translation transformations between two 3D images. ICP rotation and translation information obtain from registering the fused visible and 3D LADAR imagery was then used to calculate the x-y plane, range and intensity (xyzi) coordinates of various structures (building, vehicles, trees etc.) along the driven path. The xyzi coordinates information was then combined to create a 3D terrain map (point-cloud). In this paper, we describe the development and application of 3D imaging techniques (most specifically the ICP algorithm) used to improve spatial, range and intensity estimates of imagery collected during urban terrain mapping using a co-bore sighted, commercially available digital video camera with focal plan of 640x480 pixels and a 3D FLASH LADAR. Various representations of the reconstructed point-clouds for the drive through data will also be presented.

关键词： FLASH 3D LADAR iterative closest point algorithm terrain mapping

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3D point Cloud Registration for Localization using a Deep Neural Network Auto-Encoder 30

3D Point Cloud Registration for Localization using a Deep Ne...

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30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Elbaz, Gil Avraham, Tamar Fischer, Anath Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781538604571

We present an algorithm for registration between a large-scale point cloud and a close-proximity scanned point cloud, providing a localization solution that is fully independent of prior information about the initial positions of the two point cloud coordinate systems. The algorithm, denoted LORAX, selects super-points-local subsets of points-and describes the geometric structure of each with a low-dimensional descriptor. These descriptors are then used to infer potential matching regions for an efficient coarse registration process, followed by a fine-tuning stage. The set of super-points is selected by covering the point clouds with overlapping spheres, and then filtering out those of low-quality or nonsalient regions. The descriptors are computed using state-of-the-art unsupervised machine learning, utilizing the technology of deep neural network based auto-encoders. This novel framework provides a strong alternative to the common practice of using manually designed key-point descriptors for coarse point cloud registration. Utilizing super-points instead of key-points allows the available geometrical data to be better exploited to find the correct transformation. Encoding local 3D geometric structures using a deep neural network auto-encoder instead of traditional descriptors continues the trend seen in other computer vision applications and indeed leads to superior results. The algorithm is tested on challenging point cloud registration datasets, and its advantages over previous approaches as well as its robustness to density changes, noise and missing data are shown.

关键词： Three-dimensional displays Neural networks algorithm design and analysis Two dimensional displays iterative closest point algorithm Computer vision Robustness

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Registration of Images to Unorganized 3D point Clouds Using Contour Cues 25

Registration of Images to Unorganized 3D Point Clouds Using ...

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25th European Signal Processing Conference (EUSIPCO)

作者： Pujol-Miro, Alba Ruiz-Hidalgo, Javier Casas, Josep R. Univ Politecn Cataluna Image Proc Grp Signal Theory & Commun Barcelona Spain

ISBN: (纸本)9780992862671

Low resolution commercial 3D sensors contribute to computer vision tasks even better when the analysis is carried out in a combination with higher resolution image data. This requires registration of 2D images to unorganized 3D point clouds. In this paper we present a framework for 2D-3D data fusion to obtain directly the camera pose of a 2D color image in relation to a 3D point cloud. It includes a novel multiscale intensity feature detection algorithm and a modified ICP procedure based on point-to-line distances. The framework is generic for several data types (such as CAD designs or LiDAR data without photometric information), and results show that performance is comparable to the state of the art, while avoiding manual markers or specific patterns on the data.

关键词： feature extraction image registration iterative closest point algorithm stereo vision

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RPM-Net: Robust point Matching using Learned Features

RPM-Net: Robust Point Matching using Learned Features

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

作者： Yew, Zi Jian Lee, Gim Hee Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

iterative closest point (ICP) solves the rigid point cloud registration problem iteratively in two steps: (1) make hard assignments of spatially closest point correspondences, and then (2) find the least-squares rigid transformation. The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which often cause ICP to converge to wrong local minima. In this paper, we propose the RPM-Net - a less sensitive to initialization and more robust deep learning-based approach for rigid point cloud registration. To this end, our network uses the differentiable Sinkhorn layer and annealing to get soft assignments of point correspondences from hybrid features learned from both spatial coordinates and local geometry. To further improve registration performance, we introduce a secondary network to predict optimal annealing parameters. Unlike some existing methods, our RPM-Net handles missing correspondences and point clouds with partial visibility. Experimental results show that our RPM-Net achieves state-of-the-art performance compared to existing non-deep learning and recent deep learning methods. Our source code is available at the project website(1).

关键词： Three-dimensional displays iterative closest point algorithm Feature extraction Robustness Annealing Noise measurement Machine learning

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An efficient point Based Registration of Intra-operative Ultrasound images with MR images for computation of brain shift;a Phantom Study

An efficient Point Based Registration of Intra-operative Ult...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Farnia, Parastoo Ahmadian, Alireza Khoshnevisan, Alireza Jaberzadeh, AmirHossein Serej, Nasim Dadashi Kazerooni, Anahita F. Univ Tehran Med Sci Dept Biomed Syst & Med Phys Tehran Iran Univ Tehran Med Sci Res Ctr Sci & Technol Med RCSTIM Tehran Iran Univ Tehran Med Sci Imam Hosp Dept Neurosurg Tehran 14174 Iran Univ Tehran Med Sci Brain & Spinal Injuries Repair Res Ctr BASIR Tehran 14174 Iran

ISBN: (纸本)9781424441228

Intra-operative brain deformation (brain shift) limits the accuracy of image-guided neuro-surgery systems. Ultrasound imaging as a simple, fast and being real time has become an alternative to MR imaging which is an expensive system for brain shift calculation. The main challenges due to speckle noise and artifacts in US images, is to perform an accurate and fast registration of Us images with pre-operative MR images. In this paper an efficient point based registration method based on the alignment of probability density functions called Coherent point Drift (CPD) is implemented and compared to the conventional ICP method. To perform this, a brain phantom that allows simulating the brain deformation is made. As the results of our phantom study confirm the CPD method clearly outperforms the ICP algorithm for brain shift calculation. Also the result proves that using intra-operative US has led to recover almost 80% of displacement in the region of interest.

关键词： Electron tubes iterative closest point algorithm MR image MR imaging Magnetic resonance imaging Phantoms Ultrasonic imaging biomedical MRI biomedical ultrasonics brain brain shift calculation coherent point drift density functional theory image artifact i

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Initialized iterative closest point for bone recognition in ultrasound volumes 23

Initialized Iterative Closest Point for bone recognition in ...

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23rd International Conference on Pattern Recognition (ICPR)

作者： Haddad, Oussama Leboucher, Julien Troccaz, Jocelyne Stindel, Eric INSERM Lab Traitement Informat Med LATIM Brest France Univ Grenoble Alpes CNRS TIMC IMAG UMR 5525 F-38041 Grenoble France Ctr Hosp Reg & Univ Serv Orthopedie Traumatol Brest France

ISBN: (纸本)9781509048472

Ultrasound (US) probes have been used as guiding tools for Computer Assisted Orthopedic Surgeries (CAOS) [1]. Because of the US data uncertainty, the process of recognition - the localization of regions of interest in the image- requires a registration to a more precise, but invasive, imaging modality such as Computed Tomography (CT). A millimetric precision and a real-time processing are intraoperative requirements. iterative closest point (ICP) [2] is a simple and non symmetric rigid registration algorithm that is sensitive to the initial position of the point sets. The aim of this study is to show the contribution of initializing ICP in rigid US-CT registration and to illustrate it on data of a proximal femur. First, an iterative initialization of the model (CT) to the partial view (US) is performed using ICP with annealed filtering. The first obtained local minimum is then used to initialize a refinement step that maps the partial view to the model. One femur phantom was imaged both in a water bath using a calibrated 3D ultrasound probe and by CT For each of the ten US acquisitions (five in the Anterior neck A, and five in the Posterior neck P), the CT scan is brought by means of fiducials pair-point matching. The initialization step improves ICP successful registrations from (A:25%, P:21%) to (A:76%, to P:52%) and the registration takes about 3s in average whilst ICP takes about Is.

关键词： iterative closest point algorithm Computed tomography Ultrasonic imaging Bones Shape Probes Three-dimensional displays

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Robust Vehicle Environment Reconstruction from point Clouds For Irregularity Detection 28

Robust Vehicle Environment Reconstruction from Point Clouds ...

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28th IEEE Intelligent Vehicles Symposium (IV)

作者： Sidiya, Ahmed C. Rubaiyat, A. H. M. Fallah, Yaser Bansal, Gaurav Shimizu, Takayuki Li, X. West Virginia Univ Lane Dept CSEE Morgantown WV 26506 USA Univ Cent Florida Dept ECE Orlando FL 32816 USA Toyota InfoTechnol Ctr Mountain View CA USA

ISBN: (纸本)9781509048045

Understanding the surrounding environment including both still and moving objects is crucial to the design and optimization of intelligent vehicles. Knowledge about the vehicle environment could facilitate reliable detection of moving objects, especially irregular events (e.g., pedestrians crossing the road, vehicles making sudden lane changes,) for the purpose of avoiding collisions. Inspired by the analogy between point cloud and video data, we propose to formulate a problem of reconstructing the vehicle environment (e.g., terrains and buildings) from a sequence of point cloud sets. Built upon existing point cloud registration tool such as iterated closest point (ICP), we have developed an expectation-maximization (EM)-ICP technique that can automatically mosaic multiple point cloud sets into a larger one characterizing the still environment surrounding the vehicle. Moreover, we propose to address the issue of irregularity detection from the extracted moving objects. Our experimental results have shown successful reconstruction of a variety of challenging vehicle environments (including rural and urban, road and intersection, etc.) and simultaneous tracking/segmentation of multiple moving objects.

关键词： Robustness Three-dimensional displays Image reconstruction Intelligent vehicles iterative closest point algorithm Hafnium compounds Roads

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