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

共 1671 条记录，以下是51-60 订阅

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Real-time 3D Reconstruction Using a Combination of point-based and Volumetric Fusion

Real-time 3D Reconstruction Using a Combination of Point-bas...

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

作者： Xia, Zhengyu Kim, Joohee Park, Young Soo IIT Chicago IL 60616 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781538680940

Real-time 3D reconstruction using low-cost commodity sensors like Kinect or Xtion has been successfully applied in a wide range of fields like augmented reality, robotic teleoperation, and medical diagnosis. Due to the assumption of static scene, popular 3D reconstruction technologies such as KinectFusion and KinFu, find truthful reconstruction with fast motion camera or segmenting a moving object to be a challenge. In this paper, we propose a weighted iterative closest point (ICP) algorithm that uses both depth and RGB information to enhance the stability of camera tracking. Additionally, a GPU-based region growing method that combines depth, normal and intensity level as similarity criteria, is also applied to segment foreground moving objects accurately. For real-time processing and GPU memory efficiency, we also design a combination of point-based and volumetric representation to reconstruct moving objects and static scene, respectively. Both qualitative and quantitative results show that our proposed method improves real-time 3D reconstruction on the performance of camera tracking and segmentation of moving objects with reduced computational complexity.

关键词： Cameras Three-dimensional displays Pose estimation Sensors Real-time systems iterative closest point algorithm Systematics

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Density Adaptive point Set Registration 31

Density Adaptive Point Set Registration

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

作者： Lawin, Felix Jaremo Danelljan, Martin Khan, Fahad Shahbaz Forssen, Per-Erik Felsberg, Michael Linkoping Univ Dept Elect Engn Comp Vis Lab Linkoping Sweden

ISBN: (纸本)9781538664209

Probabilistic methods for point set registration have demonstrated competitive results in recent years. These techniques estimate a probability distribution model of the point clouds. While such a representation has shown promise, it is highly sensitive to variations in the density of 3D points. This fundamental problem is primarily caused by changes in the sensor location across point sets. We revisit the foundations of the probabilistic registration paradigm. Contrary to previous works, we model the underlying structure of the scene as a latent probability distribution, and thereby induce invariance to point set density changes. Both the probabilistic model of the scene and the registration parameters are inferred by minimizing the Kullback-Leibler divergence in an Expectation Maximization based framework. Our density-adaptive registration successfully handles severe density variations commonly encountered in terrestrial Lidar applications. We perform extensive experiments on several challenging real world Lidar datasets. The results demonstrate that our approach outperforms state-of-the-art probabilistic methods for multi-view registration, without the need of re-sampling.

关键词： Three-dimensional displays Probabilistic logic Laser radar Adaptation models Computer vision Computational modeling iterative closest point algorithm

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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： Rozenberszki, David Majdik, Andras L. Hungarian Acad Sci Inst Comp Sci & Control Machine Percept Res Lab MTA SZTAKI H-1111 Budapest Hungary

ISBN: (纸本)9781728173955

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 Lidar-only 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 multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images 4

A multi-resolution ICP with heuristic closest point search f...

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4th International Conference on 3-D Digital Imaging and Modeling (3-DIM 2003)

作者： Jost, T Hügli, H Univ Neuchatel Inst Microtechnol CH-2000 Neuchatel Switzerland

ISBN: (纸本)0769519911

The iterative closest point (ICP) algorithm is widely used for the registration of 3D geometric data. One of the main drawbacks of the algorithm is its quadratic time complexity O(N-2) with the number of points N.:Consequently, several methods have been proposed to accelerate the process. This paper presents a new solution for the speeding up of the ICP algorithm and special care is taken to avoid any tradeoff with the quality of the registration. The proposed solution combines a coarse to fine multiresolution approach with the neighbor search algorithm. The multiresolution approach permits to successively improve the registration using finer levels of representation and the neighbor search algorithm speeds up the closest point search by using a heuristic approach. Both multiresolution scheme and neighbor search algorithm main features are presented in this paper. Confirming the success of the proposed Solution, typical results show that this combination permits to create a very fast ICP algorithm, with a closest point search complexity of O(N), while preserving the matching quality.

关键词： Acceleration Clouds Inspection iterative algorithms iterative closest point algorithm iterative methods Multiresolution analysis Object recognition Robustness Shape

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Visual Marker Guided point Cloud Registration in a Large Multi-Sensor Industrial Robot Cell 14

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

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14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Ujkani, Erind Dybedal, Joacim Aalerud, Atle Kaldestad, Knut Berg Hovland, Geir Univ Agder Dept Engn Sci Mechatron Grp Kristiansand Norway

ISBN: (纸本)9781538646434

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 x 14m x 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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A Real-Time algorithm for Mobile Robot Mapping Based on Rotation-Invariant Descriptors and iterative Close point algorithm 5th

A Real-Time Algorithm for Mobile Robot Mapping Based on Rota...

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5th International Conference on Analysis of Images, Social Networks, and Texts (AIST)

作者： Vokhmintcev, A. Yakovlev, K. Chelyabinsk State Univ Res Lab Chelyabinsk Russia Natl Res Univ Russian Acad Sci Comp Sci & Control Higher Sch Econ Moscow Russia

ISBN: (纸本)9783319529202;9783319529196

Nowadays many algorithms for mobile robot mapping in indoor environments have been created. In this work we use a Kinect 2.0 camera, a visible range cameras Beward B2720 and an infrared camera Flir Tau 2 for building 3D dense maps of indoor environments. We present the RGB-D Mapping and a new fusion algorithm combining visual features and depth information for matching images, aligning of 3D point clouds, a "loop-closure" detection, pose graph optimization to build global consistent 3D maps. Such 3D maps of environments have various applications in robot navigation, real-time tracking, non-cooperative remote surveillance, face recognition, semantic mapping. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is presented and discussed.

关键词： Fusion Simultaneous location and mapping iterative closest point algorithm Matching algorithm Histograms of oriented gradients Depth map

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iterative Global Similarity points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration 6

Iterative Global Similarity Points : A robust coarse-to-fine...

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

作者： Pan, Yue Yang, Bisheng Liang, Fuxun Dong, Zhen Wuhan Univ Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)9781538684252

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)[12] 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[39] 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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A General Framework for Flexible Multi-Cue Photometric point Cloud Registration

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

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Della Corte, Bartolomeo Bogoslavskyi, Igor Stachniss, Cyrill Grisetti, Giorgio Univ Bonn Bonn Germany Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Ruber Rome Italy

ISBN: (纸本)9781538630815

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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ECM versus ICP for point Registration

ECM versus ICP for Point Registration

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

作者： Xie, Weiguo Nolte, Lutz-Peter Zheng, Guoyan Univ Bern Inst Surg Technol & Biomech ISTB CH-3014 Bern Switzerland

ISBN: (纸本)9781424441228

iterative closest point (ICP) is a widely exploited method for point registration that is based on binary point-to-point assignments, whereas the Expectation Conditional Maximization (ECM) algorithm tries to solve the problem of point registration within the framework of maximum likelihood with point-to-cluster matching. In this paper, by fulfilling the implementation of both algorithms as well as conducting experiments in a scenario where dozens of model points must be registered with thousands of observation points on a pelvis model, we investigated and compared the performance (e. g. accuracy and robustness) of both ICP and ECM for point registration in cases without noise and with Gaussian white noise. The experiment results reveal that the ECM method is much less sensitive to initialization and is able to achieve more consistent estimations of the transformation parameters than the ICP algorithm, since the latter easily sinks into local minima and leads to quite different registration results with respect to different initializations. Both algorithms can reach the high registration accuracy at the same level, however, the ICP method usually requires an appropriate initialization to converge globally. In the presence of Gaussian white noise, it is observed in experiments that ECM is less efficient but more robust than ICP.

关键词： AWGN Accuracy algorithm design and analysis ECM algorithm Electronic countermeasures Equations Expectation Conditional Maximization Gaussian white noise ICP method iterative closest point iterative closest point algorithm Mathematical model Noise binary p

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CIRCULAR TRAJECTORY CORRESPONDENCES FOR iterative closest point REGISTRATION

CIRCULAR TRAJECTORY CORRESPONDENCES FOR ITERATIVE CLOSEST PO...

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3DTV-Conference on True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON)

作者： Suominen, Olli Gotchev, Atanas Tampere Univ Technol Dept Signal Proc FI-33101 Tampere Finland

ISBN: (纸本)9781479913695

iterative closest point (ICP) is a popular algorithm for finding rigid transformations between 3D point clouds. It aims to find the rotation and translation differences between the point clouds. A key component in the algorithm is finding correspondences between the two data sets, which are then used to determine the differences. A method for finding these pairings is described, utilizing the circular trajectory of points when the cloud is being rotated. The proposed method reveals more information per iteration cycle than techniques previously used for 3D data. The method enables the use of an efficient implementation by using a simple data structure, which has the same computational complexity to build and to access as the k-d tree commonly used with nearest neighbor correspondence searches. The experimental results show that the convergence rate is superior to the original ICP based on point-to-point minimization and compares favorably to more refined and complex approaches, e.g. normal shooting with point to plane minimization. This together with the efficient implementation strategy and low amount of computation per iteration makes the circular trajectory correspondences (CTC) a valid choice for registration tasks, especially in applications where processing power is limited.

关键词： Convergence Geometry Image registration iterative algorithms iterative closest point algorithm iterative methods

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