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International Conference on Computer Vision (ICCV)

作者： Ibrahim Jubran Alaa Maalouf Ron Kimmel Dan Feldman University of Haifa Technion

ISBN: (纸本)9781665428132

The goal of the alignment problem is to align a (given) point cloud P = {p 1 , ⋯,p n } to another (observed) point cloud Q = {q 1 , ⋯,q n }. That is, to compute a rotation matrix R ∈ ℝ 3×3 and a translation vector t ∈ ℝ 3 that minimize the sum of paired distances between every transformed point Rp i − t, to its corresponding point q i , over every i ∈ {1, ⋯,n}. A harder version is the registration problem, where the correspondence is unknown, and the minimum is also over all possible correspondence functions from P to Q. algorithms such as the iterative closest point (ICP) and its variants were suggested for these problems, but none yield a provable non-trivial approximation for the global *** prove that there always exists a "witness" set of 3 pairs in P × Q that, via novel alignment algorithm, defines a constant factor approximation (in the worst case) to this global optimum. We then provide algorithms that recover this witness set and yield the first provable constant factor approximation for the: (i) alignment problem in O(n) expected time, and (ii) registration problem in polynomial time. Such small witness sets exist for many variants including points in d-dimensional space, outlier-resistant cost functions, and different correspondence *** experimental results on real and synthetic datasets show that, in practice, our approximation constants are close to 1 and our error is up to x10 times smaller than state-of-the-art algorithms.

关键词： point cloud compression Computer vision iterative closest point algorithm Economic indicators Approximation algorithms Cost function

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A Plane-based Approach for Indoor point Clouds Registration

A Plane-based Approach for Indoor Point Clouds Registration

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International Conference on Pattern Recognition

作者： Ketty Favre Muriel Pressigout Eric Marchand Luce Morin Univ Rennes CNRS IETR - UMR 6164 Rennes France Univ Rennes INSA Rennes CNRS IETR - UMR 6164 Rennes France Univ Rennes Inria CNRS IRISA Rennes France

iterative closest point (ICP) is one of the mostly used algorithms for 3D point clouds registration. This classical approach can be impacted by the large number of points contained in a point cloud. Planar structures, which are less numerous than points, can be used in well-structured man-made environment. In this paper we propose a registration method inspired by the ICP algorithm in a plane-based registration approach for indoor environments. This method is based solely on data acquired with a LiDAR sensor. A new metric based on plane characteristics is introduced to find the best plane correspondences. The optimal transformation is estimated through a two-step minimization approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) dataset show that the proposed method enables to successfully register 100 % of the scans from the three indoor sequences. Experiments also show that the proposed method is more robust in large motion scenarios than other state-of-the-art algorithms.

关键词： Measurement Three-dimensional displays Laser radar iterative closest point algorithm Autonomous systems Minimization Registers

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Cooperative Cache-Aware Recommendation System for Multiple Internet Content Providers

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IEEE WIRELESS COMMUNICATIONS LETTERS 2020年第12期9卷 2112-2115页

作者： Zheng, Dongsheng Chen, Yingyang Yin, Mingxi Jiao, Bingli Peking Univ Dept Elect Beijing 100871 Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

Joint optimization of caching and recommendation is expected to achieve a beneficial tradeoff between caching efficiency and users' quality of experience (QoE). To this end, we study a cooperative cache-aware recommendation system (CCARS) with multiple Internet content providers (ICPs) considering content sharing among them. We first analytically derive the cache hit ratio by modeling users' preference distribution and the impact of recommendation. We then formulate a problem of maximizing the cache hit ratio by optimizing the caching placement and the recommendation policy. Finally, a heuristic algorithm with low computational complexity is proposed to solve the formulated problem. The effectiveness of our algorithm is validated and the advantage with respect to the independent cache-aware recommendation system (ICARS) for each ICP is demonstrated. Numerical results show that the cache hit ratio benefits from the increased cache size at base station (BS) and the augmented content sharing among ICPs.

关键词： iterative closest point algorithm Distortion Optimization Indexes Libraries Cache storage Internet Caching recommendation content sharing cache hit ratio

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Requirement Analysis of Lidar Sensor Setups for Self-Localization in Automated Valet Parking

Requirement Analysis of Lidar Sensor Setups for Self-Localiz...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Marcel Kascha Roman Henze Institute of Automotive Engineering Technische Universität Braunschweig Braunschweig Germany

In the use case of automated valet parking (AVP), localization must be accomplished based on environmental sensors due to the lack of satellite visibility. Previous research has presented suitable methods, which, on the other hand, generally apply a 360° lidar setup. In order to achieve a series introduction of AVP, it is necessary from an economic point of view to achieve self-localization with scaled-down sensor setups that correspond to the current state of series production. Addressing this challenge, this work investigates to what extent a 360° lidar reference sensor setup can be reduced in order to achieve sufficiently robust self-localization in parking garage environments. The investigations are carried out in a real environment in two parking garages in conjunction with a test vehicle equipped with a 360° lidar reference setup. On the basis of a definition of different sensor arrangements, a parameter variation is carried out. As evaluation basis for the requirement analysis, a self-localization based on a modified iterative closest point (ICP) algorithm introduced in the scope of the work is applied.

关键词： Location awareness Laser radar Satellites iterative closest point algorithm Pose estimation Production Sensor phenomena and characterization

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Sampling Network Guided Cross-Entropy Method for Unsupervised point Cloud Registration

Sampling Network Guided Cross-Entropy Method for Unsupervise...

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International Conference on Computer Vision (ICCV)

作者： Haobo Jiang Yaqi Shen Jin Xie Jun Li Jianjun Qian Jian Yang PCA Lab Nanjing University of Science and Technology China

ISBN: (纸本)9781665428132

In this paper, by modeling the point cloud registration task as a Markov decision process, we propose an end-to-end deep model embedded with the cross-entropy method (CEM) for unsupervised 3D registration. Our model consists of a sampling network module and a differentiable CEM module. In our sampling network module, given a pair of point clouds, the sampling network learns a prior sampling distribution over the transformation space. The learned sampling distribution can be used as a "good" initialization of the differentiable CEM module. In our differentiable CEM module, we first propose a maximum consensus criterion based alignment metric as the reward function for the point cloud registration task. Based on the reward function, for each state, we then construct a fused score function to evaluate the sampled transformations, where we weight the current and future rewards of the transformations. Particularly, the future rewards of the sampled transforms are obtained by performing the iterative closest point (ICP) algorithm on the transformed state. By selecting the top-k transformations with the highest scores, we iteratively update the sampling distribution. Furthermore, in order to make the CEM differentiable, we use the sparse-max function to replace the hard top-k selection. Finally, we formulate a Geman-McClure estimator based loss to train our end-to-end registration model. Extensive experimental results demonstrate the good registration performance of our method on benchmark datasets. Code is available at https://***/Jiang-HB/CEMNet.

关键词： point cloud compression Training Measurement Solid modeling Three-dimensional displays iterative closest point algorithm Transforms

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Plane-based Accurate Registration of Real-world point Clouds

Plane-based Accurate Registration of Real-world Point Clouds

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IEEE International Conference on Systems, Man and Cybernetics

ISBN: (纸本)9781665442084

Traditional 3D point clouds registration algorithms, based on iterative closest point (ICP), rely on point matching of large point clouds. In well-structured environments, such as buildings, planes can be segmented and used for registration, similarly to the classical point-based ICP approach. Using planes tremendously reduces the number of *** this article, an efficient plane-based registration algorithm is presented. The optimal transformation is estimated through a two-step approach, successively performing robust plane-to-plane minimization and non-linear robust point-to-plane registration. Experiments on the Autonomous Systems Lab (ASL) benchmark dataset show that the proposed method enables to successfully register 100% of the scans from the three indoor sequences. Experiments also show that the proposed method is robust in large motion scenarios and more accurate than other state-of-the-art algorithms. Moreover, a new challenging dataset, LOOP’IN, is provided. It is composed of two loops in real-world indoor scenes, with a large number of scans captured with a 3D LiDAR. Tests led on this dataset show that the algorithm is able to register long sequences, to close loops and to build an incremental map of the explored environment.

关键词： point cloud compression Location awareness Three-dimensional displays Simultaneous localization and mapping Laser radar iterative closest point algorithm Minimization

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An Improved ICP point Cloud Registration algorithm Based on Three-points Congruent Sets

An Improved ICP Point Cloud Registration Algorithm Based on ...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Peng Yu Yongqian Yang Aizhong Tian Changqing Du Xiaofan Liu Biying Pei Kaixin Gu Yimu Guo Songyang Che State Grid Jiangsu Electric Power Engineering Consulting Co. Nanjing China

ISBN: (纸本)9781665417334

ICP (iterative closest point) is the most widely used point cloud registration algorithm. However, some shortcomings still exist in this algorithm, such as (1) the need to manually determine the initial value of the registration; (2) the low efficiency for large-scale point cloud registration. Therefore, this paper proposes an improved ICP point cloud registration algorithm based on the three-points congruent sets. Firstly, the algorithm narrows the search of corresponding points by extracting 3D-SIFT key points. Then, possible corresponding points are confirmed by the position relationship between the centroid and key points. The optimal transformation matrix can also be determined based on the error function. Finally, the two point clouds are accurately aligned according to the resulted optimal transformation matrix and ICP algorithm. Experimentally, the algorithm is proved to be efficient without manual intervention.

关键词： point cloud compression iterative closest point algorithm Manuals Artificial intelligence

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Strip adjustment of airborne laserscanning data with conformal geometric algebra 11

Strip adjustment of airborne laserscanning data with conform...

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Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS)

作者： Hitzer, Eckhard Benger, Werner Niederwieser, Manfred Baran, Ramona Steinbacher, Frank Int Christian Univ Coll Liberal Arts Tokyo Japan Airborne HydroMapping GmbH Feldstr 1b A-6020 Innsbruck Austria Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

In this work we describe how to use the iterative closest point method for the adjustment of airborne laser scanning data strips in the framework of conformal geometric algebra, using rotors, translators, motors and d... 详细信息

ISBN: (纸本)9781728197326

关键词： Conformal geometric algebra georeferencing iterative closest point algorithm orientation calibration

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An automatic method for the measurement of coarse particle movement in a mountain riverbed

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MEASUREMENT 2021年 174卷 109029-109029页

作者： Walicka, Agata Pfeifer, Norbert Borkowski, Andrzej Jozkow, Grzegorz Wroclaw Univ Environm & Life Sci Inst Geodesy & Geoinformat Grunwaldzka 53 PL-50357 Wroclaw Poland TU Wien Dept Geodesy & Geoinformat Wiedner Hauptstr 8 Vienna Austria

In this paper, we propose an automatic approach that is able to recognize the corresponding particles in multitemporal point clouds and to determine 3D displacement vectors and the rotation parameters between them. The core of this method is a moving window approach combined with the iterative closest point (ICP) algorithm. The particle-wise ICP algorithm is supplemented by initial transformation parameters obtained by key point matching. The routine is designed to be applied to natural objects, which are characterized by their complex geometry. The method?s performance is verified using terrestrial laser scanning data sets representing a mountain riverbed. The experiments performed show that the method enables us to recognize corresponding particles with an effectiveness of 85%. The mean absolute distances between the tile of a point cloud and the particle after alignment are used as the accuracy of the alignment. The median of these values is equal to 2 mm.

关键词： iterative closest point algorithm Terrestrial laser scanning Morphodynamics Displacement

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3D Reconstruction of Edge Line by ICP-based Matching with Geometric Constraints

3D Reconstruction of Edge Line by ICP-based Matching with Ge...

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Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Takeyama, Kojiro Toyota Cent Res & Dev Labs Inc Nagakute Aichi Japan

ISBN: (纸本)9781728191089

This paper presents a novel edge-based 3D reconstruction method using a monocular camera. The edge information is known to be illumination-invariant and to include abundant structural information in a relatively small number of pixels. However, since edge line cannot explicitly determine the pixel-to-pixel correspondence as in the feature point approach, it is difficult to perform accurate matching of pixels in a scene with dense edge lines. In this study, edge-based 3D reconstruction using ICP (iterative closest point algorithm) with geometric constraints has been proposed. In our approach, quasi-rigid body assumption for the edge line deformation and smart search for the matching process are introduced for the improvement of matching robustness in scenes with dense edge lines. Experimental results show that the performance of our method for both motion parallax estimation and depth estimation is greatly improved compared with two recent edge-based methods.

关键词： SFM spatial map, edge line monocular camera iterative closest point algorithm

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