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

共 1674 条记录，以下是221-230 订阅

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EOE: Expected Overlap Estimation over Unstructured point Cloud Data

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

引用

International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Benjamin Eckart Kihwan Kim Kautz Jan NVIDIA Research

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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A General Framework for Flexible Multi-Cue Photometric point Cloud Registration

A General Framework for Flexible Multi-Cue Photometric Point...

引用

IEEE International Conference on Robotics and Automation

作者： Bartolomeo Della Corte Igor Bogoslavskyi Cyrill Stachniss Giorgio Grisetti Department of Computer Control and Management Engineering Antonio Ruberti Rome Italy

The ability to build maps is a key functionality for the majority of mobile robots. A central ingredient to most mapping systems is the registration or alignment of the recorded sensor data. In this paper, we present a general methodology for photometric registration that can deal with multiple different cues. We provide examples for registering RGBD as well as 3D LIDAR data. In contrast to popular point cloud registration approaches such as ICP our method does not rely on explicit data association and exploits multiple modalities such as raw range and image data streams. Color, depth, and normal information are handled in an uniform manner and the registration is obtained by minimizing the pixel-wise difference between two multi-channel images. We developed a flexible and general framework and implemented our approach inside that framework. We also released our implementation as open source C++ code. The experiments show that our approach allows for an accurate registration of the sensor data without requiring an explicit data association or model-specific adaptations to datasets or sensors. Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.

关键词： Three-dimensional displays Robot sensing systems Cameras iterative closest point algorithm Minimization Integrated circuit modeling Laser radar

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NRGA: Gravitational Approach for Non-rigid point Set Registration

NRGA: Gravitational Approach for Non-rigid Point Set Registr...

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International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Sk Aziz Ali Vladislav Golyanik Didier Stricker University of Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI GmbH)

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 non-rigidly 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 data sets. 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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iterative Global Similarity points: A Robust Coarse-to-Fine Integration Solution for Pairwise 3D point Cloud Registration

Iterative Global Similarity Points: A Robust Coarse-to-Fine ...

引用

International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)

作者： Yue Pan Bisheng Yang Fuxun Liang Zhen Dong Wuhan University Wuhan China

In this paper, we propose a coarse-to-fine integration solution inspired by the classical ICP algorithm, to pairwise 3D point cloud registration with two improvements of hybrid metric spaces (e.g., BSC feature and Euclidean geometry spaces) and globally optimal correspondences matching. First, we detect the keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to encode their local features. Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the globally optimal correspondences through optimizing the energy function by the Kuhn-Munkres algorithm and then calculate the transformation based on the correspondences. Finally, we iteratively refine the transformation between two point clouds by conducting optimal correspondences matching and transformation calculation in a mutually reinforcing manner, to achieve the coarse-to-fine registration under an unified framework. The proposed method is evaluated and compared to several state-of-the-art methods on selected challenging datasets with repetitive, symmetric and incomplete structures. Comprehensive experiments demonstrate that the proposed IGSP algorithm obtains good performance and outperforms the state-of-the-art methods in terms of both rotation and translation errors.

关键词： Three-dimensional displays Feature extraction iterative closest point algorithm Bipartite graph Task analysis Extraterrestrial measurements

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Object Modeling from 3D point Cloud Data for Self-Driving Vehicles

Object Modeling from 3D Point Cloud Data for Self-Driving Ve...

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IEEE Symposium on Intelligent Vehicle

作者： Shoaib Azam Farzeen Munir Aasim Rafique YeongMin Ko Ahmad Muqeem Sheri Moongu Jeon School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology South Korea Department of Computer Science Quaid-e-Azam University Pakistan

For autonomous vehicles to be deployed and used practically, many problems are still needed to be solved. One of them we are interested in is to make use of a cheap LIDAR for robust object modelling with 3D point cloud data. Self-driving vehicles require accurate information about the surrounding environments to decide the next course of actions. 3D point cloud data obtained from LIDAR give more accurate distance than the counterpart stereo images. As LIDAR generates lowresolution data, the object detection and modeling is prone to produce errors. In this work, we propose the use of multiple frames of LIDAR data in an urban environment to construct a comprehensive model of the object. We assume the use of LIDAR on a moving platform and the results are almost equal to the 3D CAD model representation of the object.

关键词： Three-dimensional displays Laser radar Solid modeling Data models Cameras Data mining iterative closest point algorithm

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3D Rigid Registration of Cad point-Clouds

3D Rigid Registration of Cad Point-Clouds

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International Conference on Computing Sciences and Engineering (ICCSE)

作者： Ammar Hattab Gabriel Taubin School of Engineering Brown University Providence RI USA

ISBN: (纸本)9781538646816

In this paper, we introduce a new method for the rough alignment of point-clouds. We focus on a special type of point-clouds that is composed of simple geometric shapes like planes, cylinders, cones, etc. We call them 3D CAD point clouds. They are usually used in industrial and mechanical applications. The proposed method starts by detecting basic shapes in the point-clouds. And then using them to find the best transformation (rotation and translation) that aligns the point- clouds. Then, we run the fine alignment step using the iterative closest point method (ICP). We show several real-world examples of point-clouds before and after the alignment using this method. The results suggest that the proposed method works well in most cases given enough overlap between the point-clouds.

关键词： Shape Three-dimensional displays iterative closest point algorithm Fitting Manuals Cloud computing Surface fitting

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Visual Marker Guided point Cloud Registration in a Large Multi-Sensor Industrial Robot Cell

Visual Marker Guided Point Cloud Registration in a Large Mul...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Erind Ujkani Joacim Dybedal Atle Aalerud Knut Berg Kaldestad Geir Hovland Department of Engineering Sciences Mechatronics Group University of Agder Norway

This paper presents a benchmark and accuracy analysis of 3D sensor calibration in a large industrial robot cell. The sensors used were the Kinect v2 which contains both an RGB and an IR camera measuring depth based on the time-of-flight principle. The approach taken was based on a novel procedure combining Aruco visual markers, methods using region of interest and iterative closest point. The calibration of sensors is performed pairwise, exploiting the fact that time-of-flight sensors can have some overlap in the generated point cloud data. For a volume measuring 10m × 14m × 5m a typical accuracy of the generated point cloud data of 5-10cm was achieved using six sensor nodes.

关键词： Cameras Three-dimensional displays Calibration Robot sensing systems Cloud computing iterative closest point algorithm Visualization

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Bi-Direction ICP: Fast Registration Method of point Clouds 15

Bi-Direction ICP: Fast Registration Method of Point Clouds

引用

15th IAPR International Conference on Machine Vision Applications (MVA)

作者： Guan, Shiqiang Li, Guolin Xie, Xiang Wang, Zhihua Tsinghua Univ Inst Microelect Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (纸本)9784901122160

iterative closest point is a vital method in registration of point clouds. It runs slowly in case of large point clouds. In this article, we propose the Bi-Direction ICP in a 2D plane. The rotation model is used to complete Bi-Direction ICP in 3D space. Bi-Direction ICP decreases the iteration times by expanding each step angle width. This method concentrates on shortening the running time of ICP without losing the accuracy. Applied Bi-Direction ICP into Stanford 3D Scanning Repository and Mian's datasets, the registration running speed is 2.7 times at most of that in ICP. A subtle improvement occurs in the aspect of accuracy, compared to ICP.

关键词： iterative closest point algorithm Three-dimensional displays Bidirectional control Two dimensional displays Spinning Solid modeling Organizations

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Inclusion of Angular Momentum During Planning for Capture point Based Walking

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

引用

IEEE International Conference on Robotics and Automation

作者： Tim Seyde Apoorv Shrivastava Johannes Englsberger Sylvain Bertrand Jerry Pratt Robert J. Griffin Florida Institute for Human and Machine Cognition (IHMC) Pensacola FL United States Institute of Robotics and Mechatronics German Aerospace Center (DLR) Germany

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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AA-ICP: iterative closest point with Anderson Acceleration

AA-ICP: Iterative Closest Point with Anderson Acceleration

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Artem L. Pavlov Grigory WV. Ovchinnikov Dmitry Yu. Derbyshev Dzmitry Tsetserukou Ivan V. Oseledets Space Center Skolkovo Innovation Center Moscow Russia Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow Institute of Physics and Technology Space Center Skolkovo Institute of Science and Technology Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology

iterative closest point (ICP) is a widely used method for performing scan-matching and registration. Being simple and robust, this method is still computationally expensive and may be challenging to use in real-time applications with limited resources on mobile platforms. In this paper we propose a novel effective method for acceleration of ICP which does not require substantial modifications to the existing code. This method is based on an idea of Anderson acceleration which is an iterative procedure for finding a fixed point of contractive mapping. The latter is often faster than a standard Picard iteration, usually used in ICP implementations. We show that ICP, being a fixed point problem, can be significantly accelerated by this method enhanced by heuristics to improve overall robustness. We implement proposed approach into point Cloud Library (PCL) and make it available online. Benchmarking on the real-world data fully supports our claims.

关键词： iterative closest point algorithm Acceleration History Convergence Three-dimensional displays Robots Two dimensional displays

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