Relevant bibliographies by topics / Iterative Closest Point algoritmus

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Academic literature on the topic 'Iterative Closest Point algoritmus'

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Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Iterative Closest Point algoritmus"

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Choi, Ouk, Min-Gyu Park, and Youngbae Hwang. "Iterative K-Closest Point Algorithms for Colored Point Cloud Registration." Sensors 20, no. 18 (September 17, 2020): 5331. http://dx.doi.org/10.3390/s20185331.

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We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.
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Liu, Huikai, Yue Zhang, Linjian Lei, Hui Xie, Yan Li, and Shengli Sun. "Hierarchical Optimization of 3D Point Cloud Registration." Sensors 20, no. 23 (December 7, 2020): 6999. http://dx.doi.org/10.3390/s20236999.

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Rigid registration of 3D point clouds is the key technology in robotics and computer vision. Most commonly, the iterative closest point (ICP) and its variants are employed for this task. These methods assume that the closest point is the corresponding point and lead to sensitivity to the outlier and initial pose, while they have poor computational efficiency due to the closest point computation. Most implementations of the ICP algorithm attempt to deal with this issue by modifying correspondence or adding coarse registration. However, this leads to sacrificing the accuracy rate or adding the algorithm complexity. This paper proposes a hierarchical optimization approach that includes improved voxel filter and Multi-Scale Voxelized Generalized-ICP (MVGICP) for 3D point cloud registration. By combining traditional voxel sampling with point density, the outlier filtering and downsample are successfully realized. Through multi-scale iteration and avoiding closest point computation, MVGICP solves the local minimum problem and optimizes the operation efficiency. The experimental results demonstrate that the proposed algorithm is superior to the current algorithms in terms of outlier filtering and registration performance.
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Feng, Youyang, Qing Wang, and Hao Zhang. "Total Least-Squares Iterative Closest Point Algorithm Based on Lie Algebra." Applied Sciences 9, no. 24 (December 7, 2019): 5352. http://dx.doi.org/10.3390/app9245352.

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In geodetic surveying, input data from two coordinates are needed to compute rigid transformations. A common solution is a least-squares algorithm based on a Gauss–Markov model, called iterative closest point (ICP). However, the error in the ICP algorithm only exists in target coordinates, and the algorithm does not consider the source model’s error. A total least-squares (TLS) algorithm based on an errors-in-variables (EIV) model is proposed to solve this problem. Previous total least-squares ICP algorithms used a Euler angle parameterization method, which is easily affected by a gimbal lock problem. Lie algebra is more suitable than the Euler angle for interpolation during an iterative optimization process. In this paper, Lie algebra is used to parameterize the rotation matrix, and we re-derive the TLS algorithm based on a GHM (Gauss–Helmert model) using Lie algebra. We present two TLS-ICP models based on Lie algebra. Our method is more robust than previous TLS algorithms, and it suits all kinds of transformation matrices.
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Sun, Jin, Zedong Huang, Xinglong Zhu, Li Zeng, Yuan Liu, and Jing Ding. "Deformation corrected workflow for maxillofacial prosthesis modelling." Advances in Mechanical Engineering 9, no. 2 (February 2017): 168781401769228. http://dx.doi.org/10.1177/1687814017692286.

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The purpose of this article is to describe a deformation corrected workflow for maxillofacial prosthesis modelling based on the improved Laplace and iterative closest point–based iterative algorithms. For incomplete maxillofacial data with local deformed symmetrical features, the Laplace algorithm with rotation invariants was demonstrated that the operations can recover the local deformation while preserving the surface geometric detail; the M-estimation iterative closest point–based iterative algorithm integrated with the extended Gaussian image ensures the precision of the symmetry plane, making the outer point having almost no effect on the minimum process. The additional experiments also verified the ability of deformation corrected maxillofacial prosthesis modelling. Case study confirmed that this workflow is attractive and has potential to design the desired maxillofacial prosthesis for correcting the deformed oral soft tissue. The results of this study improve the quality of maxillofacial prostheses modelling. This technique will facilitate modelling of maxillofacial prostheses while helping the patients predict the effect before the prosthesis is manufactured. In addition, this deformation corrected workflow has great potential for improving the development of maxillofacial prosthesis modelling software.
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Wujanz, Daniel, Michael Avian, Daniel Krueger, and Frank Neitzel. "Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring." Earth Surface Dynamics 6, no. 2 (April 16, 2018): 303–17. http://dx.doi.org/10.5194/esurf-6-303-2018.

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Abstract. Current research questions in the field of geomorphology focus on the impact of climate change on several processes subsequently causing natural hazards. Geodetic deformation measurements are a suitable tool to document such geomorphic mechanisms, e.g. by capturing a region of interest with terrestrial laser scanners which results in a so-called 3-D point cloud. The main problem in deformation monitoring is the transformation of 3-D point clouds captured at different points in time (epochs) into a stable reference coordinate system. In this contribution, a surface-based registration methodology is applied, termed the iterative closest proximity algorithm (ICProx), that solely uses point cloud data as input, similar to the iterative closest point algorithm (ICP). The aim of this study is to automatically classify deformations that occurred at a rock glacier and an ice glacier, as well as in a rockfall area. For every case study, two epochs were processed, while the datasets notably differ in terms of geometric characteristics, distribution and magnitude of deformation. In summary, the ICProx algorithm's classification accuracy is 70 % on average in comparison to reference data.
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Cutter, Jennifer R., Iain B. Styles, Aleš Leonardis, and Hamid Dehghani. "Image-based Registration for a Neurosurgical Robot: Comparison Using Iterative Closest Point and Coherent Point Drift Algorithms." Procedia Computer Science 90 (2016): 28–34. http://dx.doi.org/10.1016/j.procs.2016.07.006.

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Wu, Lu-shen, Guo-lin Wang, and Yun Hu. "Iterative closest point registration for fast point feature histogram features of a volume density optimization algorithm." Measurement and Control 53, no. 1-2 (January 2020): 29–39. http://dx.doi.org/10.1177/0020294019878869.

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Motivated by the high speed but insufficient precision of the existing fast point feature histogram algorithm, a new fast point feature histogram registration algorithm based on density optimization is proposed. In this method, a 44-section blank feature histogram is first established, and then a principal component analysis is implemented to calculate the normal of each point in the point cloud. By translating the coordinate system in the established local coordinate system, the normal angle of each point pair and its weighted neighborhood are obtained, and then a fast point feature histogram with 33 sections is established. The reciprocal of the volume density for the central point and its weighted neighborhood are calculated simultaneously. The whole reciprocal space is divided into 11 sections. Thus, a density fast point feature histogram with 44 sections is obtained. On inputting the testing models, the initial pose of the point cloud is adjusted using the traditional fast point feature histogram and the proposed algorithms, respectively. Then, the iterative closest point algorithm is incorporated to complete the fine registration test. Compared with the traditional fine registration test algorithm, the proposed optimization algorithm can obtain 44 feature parameters under the condition of a constant time complexity. Moreover, the proposed optimization algorithm can reduce the standard deviation by 8.6% after registration. This demonstrates that the proposed method encapsulates abundant information and can achieve a high registration accuracy.
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Martínez, Jorge L., Javier González, Jesús Morales, Anthony Mandow, and Alfonso J. García-Cerezo. "Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms." Journal of Field Robotics 23, no. 1 (January 2006): 21–34. http://dx.doi.org/10.1002/rob.20104.

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Bedkowski, Janusz, Timo Röhling, Frank Hoeller, Dirk Shulz, and Frank E. Schneider. "Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems." Foundations of Computing and Decision Sciences 42, no. 3 (September 1, 2017): 275–95. http://dx.doi.org/10.1515/fcds-2017-0014.

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AbstractThis work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.
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Ning, Zhixiong, Xin Wang, Jun Wang, and Huafeng Wen. "Vehicle pose estimation algorithm for parking automated guided vehicle." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989133. http://dx.doi.org/10.1177/1729881419891335.

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Parking automated guided vehicle is more and more widely used for efficient automatic parking and one of the tough challenges for parking automated guided vehicle is the problem of vehicle pose estimation. The traditional algorithms rely on the profile information of vehicle body and sensors are required to be mounted at the top of the vehicle. However, the sensors are always mounted at a lower place because the height of a parking automated guided vehicle is always beyond 0.2mm, where we can only get the vehicle wheel information and limited vehicle body information. In this article, a novel method is given based on the symmetry of wheel point clouds collected by 3-D lidar. Firstly, we combine cell-based method with support vector machine classifier to segment ground point clouds. Secondly, wheel point clouds are segmented from obstacle point clouds and their symmetry are corrected by iterative closest point algorithm. Then, we estimate the vehicle pose by the symmetry plane of wheel point clouds. Finally, we compare our method with registration method that combines sample consensus initial alignment algorithm and iterative closest point algorithm. The experiments have been carried out.
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Dissertations / Theses on the topic "Iterative Closest Point algoritmus"

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Babin, Philippe. "Analysis of error functions for the iterative closest point algorithm." Master's thesis, Université Laval, 2019. http://hdl.handle.net/20.500.11794/37990.

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Dans les dernières années, beaucoup de progrès a été fait dans le domaine des voitures autonomes. Plusieurs grandes compagnies travaillent à créer un véhicule robuste et sûr. Pour réaliser cette tâche, ces voitures utilisent un lidar pour la localisation et pour la cartographie. Iterative Closest Point (ICP)est un algorithme de recalage de points utilisé pour la cartographie basé sur les lidars. Ce mémoire explore des approches pour améliorer le minimisateur d’erreur d’ICP. La première approche est une analyse en profondeur des filtres à données aberrantes. Quatorze des filtres les plus communs (incluant les M-estimateurs) ont été testés dans différents types d’environnement, pour un total de plus de 2 millions de recalages. Les résultats expérimentaux montrent que la plupart des filtres ont des performances similaires, s’ils sont correctement paramétrés. Néanmoins, les filtres comme Var.Trim., Cauchy et Cauchy MAD sont plus stables à travers tous les types environnements testés. La deuxième approche explore les possibilités de la cartographie à grande échelle à l’aide de lidar dans la forêt boréale. La cartographie avec un lidar est souvent basée sur des techniques de Simultaneous Localization and Mapping (SLAM) utilisant un graphe de poses, celui-ci fusionne ensemble ICP, les positions Global Navigation Satellite System (GNSS) et les mesures de l’Inertial Measurement Unit (IMU). Nous proposons une approche alternative qui fusionne ses capteurs directement dans l’étape de minimisation d’ICP. Nous avons réussi à créer une carte ayant 4.1 km de tracés de motoneige et de chemins étroits. Cette carte est localement et globalement cohérente.
In recent years a lot of progress has been made in the development of self-driving cars. Multiple big companies are working on creating a safe and robust autonomous vehicle . To make this task possible, theses vehicles rely on lidar sensors for localization and mapping. Iterative Closest Point (ICP) is a registration algorithm used in lidar-based mapping. This thesis explored approaches to improve the error minimization of ICP. The first approach is an in-depth analysis of outlier filters. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. The experimental results show that most outlier filters have a similar performance if they are correctly tuned. Nonetheless, filters such as Var.Trim., Cauchy, and Cauchy MAD are more stable against different environment types. The second approach explores the possibilities of large-scale lidar mapping in a boreal forest. Lidar mapping is often based on the SLAM technique relying on pose graph optimization, which fuses the ICP algorithm, GNSS positioning, and IMU measurements. To handle those sensors directly within theICP minimization process, we propose an alternative technique of embedding external constraints. We manage to create a crisp and globally consistent map of 4.1 km of snowmobile trails and narrow walkable trails. These two approaches show how ICP can be improved through the modification of a single step of the ICP’s pipeline.
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Landry, David. "Data-driven covariance estimation for the iterative closest point algorithm." Master's thesis, Université Laval, 2019. http://hdl.handle.net/20.500.11794/34734.

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Les nuages de points en trois dimensions sont un format de données très commun en robotique mobile. Ils sont souvent produits par des capteurs spécialisés de type lidar. Les nuages de points générés par ces capteurs sont utilisés dans des tâches impliquant de l’estimation d’état, telles que la cartographie ou la localisation. Les algorithmes de recalage de nuages de points, notamment l’algorithme ICP (Iterative Closest Point), nous permettent de prendre des mesures d’égo-motion nécessaires à ces tâches. La fusion des recalages dans des chaînes existantes d’estimation d’état dépend d’une évaluation précise de leur incertitude. Cependant, les méthodes existantes d’estimation de l’incertitude se prêtent mal aux données en trois dimensions. Ce mémoire vise à estimer l’incertitude de recalages 3D issus d’Iterative Closest Point (ICP). Premièrement, il pose des fondations théoriques desquelles nous pouvons articuler une estimation de la covariance. Notamment, il révise l’algorithme ICP, avec une attention spéciale sur les parties qui sont importantes pour l’estimation de la covariance. Ensuite, un article scientifique inséré présente CELLO-3D, notre algorithme d’estimation de la covariance d’ICP. L’article inséré contient une validation expérimentale complète du nouvel algorithme. Il montre que notre algorithme performe mieux que les méthodes existantes dans une grande variété d’environnements. Finalement, ce mémoire est conclu par des expérimentations supplémentaires, qui sont complémentaires à l’article.
Three-dimensional point clouds are an ubiquitous data format in robotics. They are produced by specialized sensors such as lidars or depth cameras. The point clouds generated by those sensors are used for state estimation tasks like mapping and localization. Point cloud registration algorithms, such as Iterative Closest Point (ICP), allow us to make ego-motion measurements necessary to those tasks. The fusion of ICP registrations in existing state estimation frameworks relies on an accurate estimation of their uncertainty. Unfortunately, existing covariance estimation methods often scale poorly to the 3D case. This thesis aims to estimate the uncertainty of ICP registrations for 3D point clouds. First, it poses theoretical foundations from which we can articulate a covariance estimation method. It reviews the ICP algorithm, with a special focus on the parts of it that are pertinent to covariance estimation. Then, an inserted article introduces CELLO-3D, our data-driven covariance estimation method for ICP. The article contains a thorough experimental validation of the new algorithm. The latter is shown to perform better than existing covariance estimation techniques in a wide variety of environments. Finally, this thesis comprises supplementary experiments, which complement the article.
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Jež, Ondřej. "Navigation of Mobile Robots in Unknown Environments Using Range Measurements." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-233443.

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The ability of a robot to navigate itself in the environment is a crucial step towards its autonomy. Navigation as a subtask of the development of autonomous robots is the subject of this thesis, focusing on the development of a method for simultaneous localization an mapping (SLAM) of mobile robots in six degrees of freedom (DOF). As a part of this research, a platform for 3D range data acquisition based on a continuously inclined laser rangefinder was developed. This platform is presented, evaluating the measurements and also presenting the robotic equipment on which the platform can be fitted. The localization and mapping task is equal to the registration of multiple 3D images into a common frame of reference. For this purpose, a method based on the Iterative Closest Point (ICP) algorithm was developed. First, the originally implemented SLAM method is presented, focusing on the time-wise performance and the registration quality issues introduced by the implemented algorithms. In order to accelerate and improve the quality of the time-demanding 6DOF image registration, an extended method was developed. The major extension is the introduction of a factorized registration, extracting 2D representations of vertical objects called leveled maps from the 3D point sets, ensuring these representations are 3DOF invariant. The extracted representations are registered in 3DOF using ICP algorithm, allowing pre-alignment of the 3D data for the subsequent robust 6DOF ICP based registration. The extended method is presented, showing all important modifications to the original method. The developed registration method was evaluated using real 3D data acquired in different indoor environments, examining the benefits of the factorization and other extensions as well as the performance of the original ICP based method. The factorization gives promising results compared to a single phase 6DOF registration in vertically structured environments. Also, the disadvantages of the method are discussed, proposing possible solutions. Finally, the future prospects of the research are presented.
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Ricci, Francesco. "Un algoritmo per la localizzazione accurata di oggetti in immagini mediante allineamento dei contorni." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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In scenari applicativi dove si vuole localizzare in modo accurato un determinato Pattern all’interno di un’immagine è necessario effettuare una fase di raffinamento della posa (Pose Refinement), in modo da incrementare la precisione dell’algoritmo di Pattern Matching. In questo lavoro è stato sviluppato un nuovo algoritmo per il Pose Refinement (denominato PR-ICP) basato esclusivamente sui punti di Edge e quindi strettamente legato al problema della registrazione di punti. Questa tipologia di algoritmo fornisce innumerevoli vantaggi rendendo l’intera operazione di Pattern Matching performante anche in scenari dove i classici approcci basati su correlazione falliscono. D’altra parte utilizzare i punti di Edge introduce diverse problematiche relative alle operazioni di Edge Detection che è necessario effettuare sul Template e sulla Search Image. Rispetto ai classici metodi basati su correlazione, il PR-ICP è più generale e invariante a variazioni di intensità luminosa tra il Template e l’oggetto nella Search Image; grazie agli Score che fornisce in output, inoltre, il PR-ICP è flessibile in quanto può avere un diverso comportamento in base allo specifico scenario applicativo impostando opportuni parametri.
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Belshaw, Michael Sweeney. "A high-speed Iterative Closest Point tracker on an FPGA platform." Thesis, Kingston, Ont. : [s.n.], 2008. http://hdl.handle.net/1974/1322.

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Graehling, Quinn R. "Feature Extraction Based Iterative Closest Point Registration for Large Scale Aerial LiDAR Point Clouds." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1607380713807017.

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Guimarães, A. A. R. "Correspondência entre regiões de imagens por meio do algoritmo iterative closet point (ICP)/." reponame:Biblioteca Digital de Teses e Dissertações da FEI, 2015. http://sofia.fei.edu.br:8080/pergamumweb/vinculos/000010/000010fb.pdf.

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Pitcher, Courtney Richard. "Matching optical coherence tomography fingerprint scans using an iterative closest point pipeline." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33923.

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Identifying people from their fingerprints is based on well established technology. However, a number of challenges remain, notably overcoming the low feature density of the surface fingerprint and suboptimal feature matching. 2D contact based fingerprint scanners offer low security performance, are easy to spoof, and are unhygienic. Optical Coherence Tomography (OCT) is an emerging technology that allows a 3D volumetric scan of the finger surface and its internal microstructures. The junction between the epidermis and dermis - the internal fingerprint - mirrors the external fingerprint. The external fingerprint is prone to degradation from wear, age, or disease. The internal fingerprint does not suffer these deficiencies, which makes it a viable candidate zone for feature extraction. We develop a biometrics pipeline that extracts and matches features from and around the internal fingerprint to address the deficiencies of contemporary 2D fingerprinting. Eleven different feature types are explored. For each type an extractor and Iterative Closest Point (ICP) matcher is developed. ICP is modified to operate in a Cartesiantoroidal space. Each of these features are matched with ICP against another matcher, if one existed. The feature that has the highest Area Under the Curve (AUC) on an Receiver Operating Characteristic of 0.910 is a composite of 3D minutia and mean local cloud, followed by our geometric properties feature, with an AUC of 0.896. By contrast, 2D minutiae extracted from the internal fingerprint achieved an AUC 0.860. These results make our pipeline useful in both access control and identification applications. ICP offers a low False Positive Rate and can match ∼30 composite 3D minutiae a second on a single threaded system, which is ideal for access control. Identification systems require a high True Positive and True Negative Rate, in addition time is a less stringent requirement. New identification systems would benefit from the introduction of an OCT based pipeline, as all the 3D features we tested provide more accurate matching than 2D minutiae. We also demonstrate that ICP is a viable alternative to match traditional 2D features (minutiae). This method offers a significant improvement over the popular Bozorth3 matcher, with an AUC of 0.94 for ICP versus 0.86 for Bozorth3 when matching a highly distorted dataset generated with SFinGe. This compatibility means that ICP can easily replace other matchers in existing systems to increase security performance.
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Morency, Louis-Philippe 1977. "Stereo-based head pose tracking using Iterative Closest Point and normal flow constraint." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87241.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
Includes bibliographical references (p. 67-71).
by Louis-Philippe Morency.
S.M.
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Pereira, Nícolas Silva. "Cloud Partitioning Iterative Closest Point (CP-ICP): um estudo comparativo para registro de nuvens de pontos 3D." reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/22971.

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PEREIRA, Nicolas Silva. Cloud Partitioning Iterative Closest Point (CP-ICP): um estudo comparativo para registro de nuvens de pontos 3D. 2016. 69 f. Dissertação (Mestrado em Engenharia de Teleinformática)–Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016.
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In relation to the scientific and technologic evolution of equipment such as cameras and image sensors, the computer vision presents itself more and more as a consolidated engineering solution to issues in diverse fields. Together with it, due to the 3D image sensors dissemination, the improvement and optimization of techniques that deals with 3D point clouds registration, such as the classic algorithm Iterative Closest Point (ICP), appear as fundamental on solving problems such as collision avoidance and occlusion treatment. In this context, this work proposes a sampling technique to be used prior to the ICP algorithm. The proposed method is compared to other five variations of sampling techniques based on three criteria: RMSE (root mean squared error), based also on an Euler angles analysis and an autoral criterion based on structural similarity index (SSIM). The experiments were developed on four distincts 3D models from two databases, and shows that the proposed technique achieves a more accurate point cloud registration in a smaller time than the other techniques.
Com a evolução científica e tecnológica de equipamentos como câmeras e sensores de imagens, a visão computacional se mostra cada vez mais consolidada como solução de engenharia para problemas das mais diversas áreas. Associando isto com a disseminação dos sensores de imagens 3D, o aperfeiçoamento e a otimização de técnicas que lidam com o registro de nuvens de pontos 3D, como o algoritmo clássico Iterative Closest Point (ICP), se mostram fundamentais na resolução de problemas como desvio de colisão e tratamento de oclusão. Nesse contexto, este trabalho propõe um técnica de amostragem a ser utilizada previamente ao algoritmo ICP. O método proposto é comparado com outras cinco varições de amostragem a partir de três critérios: RMSE (root mean squared error ), a partir de uma análise de ângulos de Euler e uma métrica autoral baseada no índice de structural similarity (SSIM). Os experimentos foram desenvolvidos em quatro modelos 3D distintos vindos de dois diferentes databases, e revelaram que a abordagem apresentada alcançou um registro de nuvens mais acuraz num tempo menor que as outras técnicas.
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Book chapters on the topic "Iterative Closest Point algoritmus"

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Wang, Lu, and Xiaopeng Sun. "Comparisons of Iterative Closest Point Algorithms." In Ubiquitous Computing Application and Wireless Sensor, 649–55. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9618-7_68.

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Zhang, Zhengyou. "Iterative Closest Point (ICP)." In Computer Vision, 433–34. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_179.

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Vavoulidis, C. P., and I. Pitas. "Morphological iterative closest point algorithm." In Computer Analysis of Images and Patterns, 416–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63460-6_145.

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Synave, R., P. Desbarats, and S. Gueorguieva. "Automated Trimmed Iterative Closest Point Algorithm." In Advances in Visual Computing, 489–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76856-2_48.

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Penney, G. P., P. J. Edwards, A. P. King, J. M. Blackall, P. G. Batchelor, and D. J. Hawkes. "A Stochastic Iterative Closest Point Algorithm (stochastICP)." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, 762–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_91.

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Chen, Junfen, and Bahari Belaton. "An Improved Iterative Closest Point Algorithm for Rigid Point Registration." In Communications in Computer and Information Science, 255–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45652-1_26.

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Chen, Elvis C. S., A. Jonathan McLeod, John S. H. Baxter, and Terry M. Peters. "An Iterative Closest Point Framework for Ultrasound Calibration." In Augmented Environments for Computer-Assisted Interventions, 69–79. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24601-7_8.

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Haugo, Simen, and Annette Stahl. "Iterative Closest Point with Minimal Free Space Constraints." In Advances in Visual Computing, 82–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64559-5_7.

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Robinson, Jace, Matt Piekenbrock, Lee Burchett, Scott Nykl, Brian Woolley, and Andrew Terzuoli. "Parallelized Iterative Closest Point for Autonomous Aerial Refueling." In Advances in Visual Computing, 593–602. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50835-1_53.

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Bærentzen, Jakob Andreas, Jens Gravesen, François Anton, and Henrik Aanæs. "3D Surface Registration via Iterative Closest Point (ICP)." In Guide to Computational Geometry Processing, 263–75. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4075-7_15.

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Conference papers on the topic "Iterative Closest Point algoritmus"

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Hansen, Mads Fogtmann, Morten Rufus Blas, and Rasmus Larsen. "Mahalanobis distance based iterative closest point." In Medical Imaging, edited by Josien P. W. Pluim and Joseph M. Reinhardt. SPIE, 2007. http://dx.doi.org/10.1117/12.708205.

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Wu, Yanyan, and Prabhjot Singh. "Methods to Improve the Accuracy of Registration for Multimodal Inspection of Mechanical Parts." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79159.

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Registration refers to the process of aligning corresponding features in images or point data sets in the same coordinate system. Multimodal inspection is a growing trend wherein an accurate measurement of the part is made by fusing data from different modalities. Registration is a key task in multimodal data fusion. The main problem with high-accuracy registration comes from noise inherent in the measurement data and the lack of the one-to-one correspondence in the data from different modalities. We present methods to deal with outliers and noise in the measurement data to improve registration accuracy. The proposed algorithms operate on point sets. Our method distinguishes between noise and accurate measurements using a new metric based on the intrinsic geometric characteristics of the point set, including distance, surface normal and curvature. Our method is unique in that it does not require a-priori knowledge of the noise in the measurement data, therefore fully automatic registration is enabled. The proposed methods can be incorporated into any point-based registration method. It was tested with the traditional ICP (Iterative Closest Point) algorithm with application to the data registration among point, image, and mesh data. The proposed method can be applied to both rigid and non-rigid registration.
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Reina, Giulio, Annalisa Milella, and Mario Foglia. "Vision-Based Methods for Mobile Robot Localization and Wheel Sinkage Estimation." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2188.

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External perception based on vision plays a critical role in developing improved and robust localization algorithms for mobile robots, as well as in gaining important information about the vehicle and the traversed terrain. This paper presents two novel methods to improve mobility on rough terrains by using visual input. The first method consists of a stereovision algorithm for 6-DoF ego-motion estimation, which integrates image intensity information and 3D stereo data using an Iterative Closest Point (ICP) approach. The second method aims at estimating the wheel sinkage of a mobile robot on deformable soil, based on the visual input from an onboard monocular camera, and an edge detection strategy. Both methods were implemented and experimentally validated on an all-terrain mobile robot, showing that the proposed techniques can be successfully employed to improve the performance of ground vehicles operating in uncharted environments.
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Seungpyo Hong, Heedong Ko, and Jinwook Kim. "VICP: Velocity updating iterative closest point algorithm." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509312.

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Wang, Fang, and Zijian Zhao. "A survey of iterative closest point algorithm." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243553.

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Milella, Annalisa, and Giulio Reina. "Rough Terrain Mobile Robot Localization Using Stereovision." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41397.

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Mobile robots are increasingly being used in high-risk rough terrain situations, such as reconnaissance, planetary exploration, safety and rescue applications. Conventional localization algorithms are not well suited to rough terrain, since sensor drift and the dynamic effects occurring at wheel-terrain interface, such as slipping and sinkage, largely compromise their accuracy. In this paper, we follow a novel approach for 6-DoF ego-motion estimation, using stereovision. It integrates image intensity information and 3D stereo data within an Iterative Closest Point (ICP) scheme. Neither a-priori knowledge of the motion and the terrain properties nor inputs from other sensors are required, while the only assumption is that the scene always contains visually distinctive features, which can be tracked over subsequent stereo pairs. This generates what is usually referred to as visual odometry. The paper details the various steps of the algorithm and presents the results of experimental tests performed with an all-terrain rover, proving the method to be effective and robust.
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Sommer, Naftali, Meir Feder, and Ofir Shalvi. "Finding the Closest Lattice Point by Iterative Slicing." In 2007 IEEE International Symposium on Information Theory. IEEE, 2007. http://dx.doi.org/10.1109/isit.2007.4557227.

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Suominen, Olli, and Atanas Gotchev. "Circular trajectory correspondences for iterative closest point registration." In 2013 3DTV Vision Beyond Depth (3DTV-CON). IEEE, 2013. http://dx.doi.org/10.1109/3dtv.2013.6676634.

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Hatanaka, Yuji, Mikiya Tajima, Ryo Kawasaki, Koko Saito, Kazunori Ogohara, Chisako Muramatsu, Wataru Sunayama, and Hiroshi Fujita. "Retinal biometrics based on Iterative Closest Point algorithm." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8036840.

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Pavlov, Artem L., Grigory WV Ovchinnikov, Dmitry Yu Derbyshev, Dzmitry Tsetserukou, and Ivan V. Oseledets. "AA-ICP: Iterative Closest Point with Anderson Acceleration." In 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. http://dx.doi.org/10.1109/icra.2018.8461063.

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