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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.Submitted by Hohana Sanders (hohanasanders@hotmail.com) on 2017-01-06T18:04:28Z No. of bitstreams: 1 2016_dis_nspereira.pdf: 7889549 bytes, checksum: d5299d9df9b32e2b1189eba97b03f9e1 (MD5)
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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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Monnier, Fabrice. "Amélioration de la localisation 3D de données laser terrestre à l'aide de cartes 2D ou modèles 3D." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1114/document.
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Les avancées technologiques dans le domaine informatique (logiciel et matériel) et, en particulier, de la géolocalisation ont permis la démocratisation des modèles numériques. L'arrivée depuis quelques années de véhicules de cartographie mobile a ouvert l'accès à la numérisation 3D mobile terrestre. L'un des avantages de ces nouvelles méthodes d'imagerie de l'environnement urbain est la capacité potentielle de ces systèmes à améliorer les bases de données existantes 2D comme 3D, en particulier leur niveau de détail et la diversité des objets représentés. Les bases de données géographiques sont constituées d'un ensemble de primitives géométriques (généralement des lignes en 2D et des plans ou des triangles en 3D) d'un niveau de détail grossier mais ont l'avantage d'être disponibles sur de vastes zones géographiques. Elles sont issues de la fusion d'informations diverses (anciennes campagnes réalisées manuellement, conception automatisée ou encore hybride) et peuvent donc présenter des erreurs de fabrication. Les systèmes de numérisation mobiles, eux, peuvent acquérir, entre autres, des nuages de points laser. Ces nuages laser garantissent des données d'un niveau de détail très fin pouvant aller jusqu'à plusieurs points au centimètre carré. Acquérir des nuages de points laser présente toutefois des inconvénients :- une quantité de données importante sur de faibles étendues géographiques posant des problèmes de stockage et de traitements pouvant aller jusqu'à plusieurs Téraoctet lors de campagnes d'acquisition importantes- des difficultés d'acquisition inhérentes au fait d'imager l'environnement depuis le sol. Les systèmes de numérisation mobiles présentent eux aussi des limites : en milieu urbain, le signal GPS nécessaire au bon géoréférencement des données peut être perturbé par les multi-trajets voire même stoppé lors de phénomènes de masquage GPS liés à la réduction de la portion de ciel visible pour capter assez de satellites pour en déduire une position spatiale. Améliorer les bases de données existantes grâce aux données acquises par un véhicule de numérisation mobile nécessite une mise en cohérence des deux ensembles. L'objectif principal de ce manuscrit est donc de mettre en place une chaîne de traitements automatique permettant de recaler bases de données géographiques et nuages de points laser terrestre (provenant de véhicules de cartographies mobiles) de la manière la plus fiable possible. Le recalage peut se réaliser de manière différentes. Dans ce manuscrit, nous avons développé une méthode permettant de recaler des nuages laser sur des bases de données, notamment, par la définition d'un modèle de dérive particulièrement adapté aux dérives non-linéaires de ces données mobiles. Nous avons également développé une méthode capable d'utiliser de l'information sémantique pour recaler des bases de données sur des nuages laser mobiles. Les différentes optimisations effectuées sur notre approche nous permettent de recaler des données rapidement pour une approche post-traitements, ce qui permet d'ouvrir l'approche à la gestion de grands volumes de données (milliards de points laser et milliers de primitives géométriques).Le problème du recalage conjoint a été abordé. Notre chaîne de traitements a été testée sur des données simulées et des données réelles provenant de différentes missions effectuées par l'IGNTechnological advances in computer science (software and hardware) and particularly, GPS localization made digital models accessible to all people. In recent years, mobile mapping systems has enabled large scale mobile 3D scanning. One advantage of this technology for the urban environment is the potential ability to improve existing 2D or 3D database, especially their level of detail and variety of represented objects. Geographic database consist of a set of geometric primitives (generally 2D lines and plans or triangles in 3D) with a coarse level of detail but with the advantage of being available over wide geographical areas. They come from the fusion of various information (old campaigns performed manually, automated or hybrid design) wich may lead to manufacturing errors. The mobile mapping systems can acquire laser point clouds. These point clouds guarantee a fine level of detail up to more than one points per square centimeter. But there are some disavantages :- a large amount of data on small geographic areas that may cause problems for storage and treatment of up to several Terabyte during major acquisition,- the inherent acquisition difficulties to image the environment from the ground. In urban areas, the GPS signal required for proper georeferencing data can be disturbed by multipath or even stopped when GPS masking phenomena related to the reduction of the portion of the visible sky to capture enough satellites to find a good localization. Improve existing databases through these dataset acquired by a mobile mapping system requires alignment of these two sets. The main objective of this manuscript is to establish a pipeline of automatic processes to register these datasets together in the most reliable manner. Co-registration this data can be done in different ways. In this manuscript we have focused our work on the registration of mobile laser point cloud on geographical database by using a drift model suitable for the non rigid drift of these kind of mobile data. We have also developped a method to register geographical database containing semantics on mobile point cloud. The different optimization step performed on our methods allows to register the data fast enough for post-processing pipeline, which allows the management of large volumes of data (billions of laser points and thousands geometric primitives). We have also discussed on the problem of joint deformation. Our methods have been tested on simulated data and real data from different mission performed by IGN
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Tuncer, Ozgur. "Segmentation, Registration And Visualization Of Medical Images For Treatment Planning." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1093368/index.pdf.
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Medical imaging has become the key to access inside human body for the
purpose of diagnosis and treatment planning. In order to understand the
effectiveness of planned treatment following the diagnosis, treated body part may
have to be monitored several times during a period of time. Information gained
from successive imaging of body part provides guidance to next step of treatment.
Comparison of images or datasets taken at different times requires registration of
these images or datasets since the same conditions may not be provided at all
times. Accurate segmentation of the body part under treatment is needed while
comparing medical images to achieve quantitative and qualitative measurements.
This segmentation task enables two dimensional and three dimensional
visualizations of the region which also aid in directing the planning strategy.
In this thesis, several segmentation algorithms are investigated and a hybrid
segmentation algorithm is developed in order to segment bone tissue out of head
CT slices for orthodontic treatment planning. Using the developed segmentation algorithm, three dimensional visualizations of segmented bone tissue out of head
CT slices of two patients are obtained. Visualizations are obtained using the
MATLAB Computer software’
s visualization library. Besides these, methods are developed for automatic registration of twodimensional and three-dimensional CT images taken at different time periods. These methods are applied to real and synthetic data. Algorithms and methods used in this thesis are also implemented in MATLAB computer program.
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Mackay, Neilson. "A comparison of three methods of ultrasound to computed tomography registration." Thesis, Kingston, Ont. : [s.n.], 2009. http://hdl.handle.net/1974/1666.
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Pilch, Petr. "3D mapování vnitřního prostředí senzorem Microsoft Kinect." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-231012.
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This work is focused on creating 3D maps of indoor enviroment using Microsoft Kinect sensor. The first part shows the description of Microsoft Kinect sensor, the methods for acquisition and processing of depth data and their registration using different algorithms. The second part shows application of algorithms for map registration and final 3D maps of indoor enviroment.
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Cejnog, Luciano Walenty Xavier. "Rigid registration based on local geometric dissimilarity." Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/4882.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Este trabalho visa melhorar um método clássico para o problema de registro rígido, o ICP (iterative Closest Point), fazendo com que a busca dos pontos mais próximos, uma de suas fases principais, considere informações aproximadas da geometria local de cada ponto combinadas à distância Euclidiana originalmente usada. Para isso é necessária uma etapa de pré-processamento, na qual a geometria local é estimada em tensores de orientação de segunda ordem. É definido o CTSF, um fator de similaridade entre tensores. O ICP é alterado de modo a considerar uma combinação linear do CTSF com a distância Euclidiana para estabelecer correspondências entre duas nuvens de pontos, variando os pesos relativos entre os dois fatores. Isso proporciona uma capacidade maior de convergência para ângulos maiores em relação ao ICP original, tornando o método comparável aos que constituem o estado da arte da área. Para comprovar o ganho obtido, foram realizados testes exaustivos em malhas com características geométricas variadas, para diferentes níveis de ruído aditivo, outliers e em casos de sobreposição parcial, variando os parâmetros do método de estimativa dos tensores. Foi definida uma nova base com malhas sintéticas para os experimentos, bem como um protocolo estatístico de avaliação quantitativa. Nos resultados, a avaliação foi feita de modo a determinar bons valores de parâmetros para malhas com diferentes características, e de que modo os parâmetros afetam a qualidade do método em situações com ruído aditivo, outliers, e sobreposição parcial.
This work aims to enhance a classic method for the rigid registration problem, the ICP (Iterative Closest Point), modifying one of its main steps, the closest point search, in order to consider approximated information of local geometry combined to the Euclidean distance, originally used. For this, a preprocessing stage is applied, in which the local geometry is estimated in second-order orientation tensors. We define the CTSF, a similarity factor between tensors. Our method uses a linear combination between this factor and the Euclidean distance, in order to establish correspondences, and a strategy of weight variation between both factors. This increases the convergence probability for higher angles with respect to the original ICP, making our method comparable to some of the state-of-art techniques. In order to comprove the enhancement, exhaustive tests were made in point clouds with different geometric features, with variable levels of additive noise and outliers and in partial overlapping situations, varying also the parameters of the tensor estimative method. A dataset of synthetic point clouds was defined for the experiments, as well as a statistic protocol for quantitative evaluation. The results were analyzed in order to highlight good parameter ranges for different point clouds, and how these parameters affect the behavior of the method in situations of additive noise, outliers and partial overlapping.
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Harish, Acharya Maniyoor, and Suppatarachai Sudsawat. "Improving the time frame reduction for reuse of roof rack components in cars using Case-based reasoning." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Produktutveckling - Datorstödd konstruktion, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19289.
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Now a days where technological advancements are growing at a rapid pace, it has become a common norm for all the manufacturing companies to be abreast with these advancements for being competitive in market. This thesis deals with development of one such common norm for one of the products (Roof rack component) for company Thule. The main aim of the thesis is to curtail the products lead time to market and this was achieved by using an artificial intelligence technique i.e., Case-based reasoning (CBR). Roof rack component which is mounted on car roof is mainly constituted by two parts foot pad and bracket, this thesis main interest was concerned with only brackets and its geometry. This thesis is based on contemplating the already implemented concepts in this context, designer requirements and exploring better solutions. The methods of implementation adopted here was using CBR concept which is based on indexing , retrieve, adapt, review, retain and employing these concepts in form of an algorithm. The concept for developing the algorithm was based on Iterative closest point (ICP) approach which emphasise on assigning lower weight to pairs with greater point to point distance. The results portrayed are with respect to geometry and also with respect to application interface developed, which both together provides us a better solution.
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Yamada, Fernando Akio de Araujo. "A Shape-based weighting strategy applied to the covariance estimation on the ICP." Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/4884.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
No problema de registro rígido por pares é preciso encontrar uma transformação rígida que alinha duas nuvens de pontos. A sulução clássica e mais comum é o algoritmo Iterative Closest Point (ICP). No entanto, o ICP e muitas de suas variantes requerem que as nuvens de pontos já estejam grosseiramente alinhadas. Este trabalho apresenta um método denominado Shape-based Weighting Covariance Iterative Closest Point (SWC-ICP), uma melhoria do ICP clássico. A abordagem proposta aumenta a possibilidade de alinhar corretamente duas nuvens de pontos, independente da pose inicial, mesmo quando existe apenas sobreposição parcial entre elas, ou na presença de ruído e outliers. Ela se beneficia da geometria local dos pontos, codificada em tensores de orientação de segunda ordem, para prover um segundo conjunto de correspondências para o ICP. A matriz de covariância cruzada computada a partir deste conjunto é combinada com a matriz de covariância cruzada usual, seguindo uma estratégia heurística. Para comparar o método proposto com algumas abordagens recentes, um protocolo de avaliação detalhado para registro rígido é apresentado. Os resultados mostram que o SWC-ICP está entre os melhores métodos comparados, com performance superior em situações de grande deslocamento angular, mesmo na presença de ruído e outliers.
In the pairwise rigid registration problem we need to find a rigid transformation that aligns two point clouds. The classical and most common solution is the Iterative Closest Point (ICP) algorithm. However, the ICP and many of its variants require that the point clouds are already coarsely aligned. We present in this work a method named Shape-based Weighting Covariance Iterative Closest Point (SWC-ICP), an improvement over the classical ICP. Our approach improves the possibility to correctly align two point clouds, regardless of the initial pose, even when there is only a partial overlapping between them, or in the presence of noise and outliers. It benefits from the local geometry of the points, encoded in second-order orientation tensors, to provide a second correspondences set to the ICP. The cross-covariance matrix computed from this set is combined with the usual cross-covariance matrix following a heuristic strategy. In order to compare our method with some recent approaches, we present a detailed evaluation protocol to rigid registration. Results show that the SWC-ICP is among the best methods compared, with superior performance in situations of wide angular displacement, even in situations of noise and outliers.
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Uktveris, Tomas. "Objektų Pozicijos ir Orientacijos Nustatymo Metodų Mobiliam Robotui Efektyvumo Tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140818_113353-37758.
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Šiame darbe tiriami algoritminiai sprendimai mobiliam robotui, leidžiantys aptikti ieškomą objektą bei įvertinti jo poziciją ir orientaciją erdvėje. Atlikus šios srities technologijų analizę surasta įvairių realizacijai tinkamų metodų, tačiau bendro jų efektyvumo palyginimo trūko. Siekiant užpildyti šią spragą realizuota programinė ir techninė įranga, kuria atliktas labiausiai roboto sistemoms tinkamų metodų vertinimas. Algoritmų analizė susideda iš algoritmų tikslumo ir jų veikimo spartos vertinimo panaudojant tam paprastus bei efektyvius metodus. Darbe analizuojamas objektų orientacijos nustatymas iš Kinect kameros gylio duomenų pasitelkiant ICP algoritmą. Atliktas dviejų gylio sistemų spartos ir tikslumo tyrimas parodė, jog Kinect kamera spartos atžvilgiu yra efektyvesnis bei 2-5 kartus tikslesnis sprendimas nei įprastinė stereo kamerų sistema. Objektų aptikimo algoritmų efektyvumo eksperimentuose nustatytas maksimalus aptikimo tikslumas apie 90% bei pasiekta maksimali 15 kadrų/s veikimo sparta analizuojant standartinius VGA 640x480 raiškos vaizdus. Atliktas objektų pozicijos ir orientacijos nustatymo ICP metodo efektyvumo tyrimas parodė, jog vidutinė absoliutinė pozicijos ir orientacijos nustatymo paklaida yra atitinkamai apie 3.4cm bei apie 30 laipsnių, o veikimo sparta apie 2 kadrai/s. Tolesnis optimizavimas arba duomenų kiekio minimizavimas yra būtinas norint pasiekti geresnius veikimo rezultatus mobilioje ribotų resursų roboto sistemoje. Darbe taip pat buvo sėkmingai... [toliau žr. visą tekstą]This work presents a performance analysis of the state-of-the-art computer vision algorithms for object detection and pose estimation. Initial field study showed that many algorithms for the given problem exist but still their combined comparison was lacking. In order to fill in the existing gap a software and hardware solution was created and the comparison of the most suitable methods for a robot system were done. The analysis consists of detector accuracy and runtime performance evaluation using simple and robust techniques. Object pose estimation via ICP algorithm and stereo vision Kinect depth sensor method was used in this work. A conducted two different stereo system analysis showed that Kinect achieves best runtime performance and its accuracy is 2-5 times more superior than a regular stereo setup. Object detection experiments showcased a maximum object detection accuracy of nearly 90% and speed of 15 fps for standard size VGA 640x480 resolution images. Accomplished object position and orientation estimation experiment using ICP method showed, that average absolute position and orientation detection error is respectively 3.4cm and 30 degrees while the runtime speed – 2 fps. Further optimization and data size minimization is necessary to achieve better efficiency on a resource limited mobile robot platform. The robot hardware system was also successfully implemented and tested in this work for object position and orientation detection.
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Jähn, Benjamin, Philipp Lindner, and Gerd Wanielik. "Multi-view point cloud fusion for LiDAR based cooperative environment detection." Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-188115.
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A key component for automated driving is 360◦ environment detection. The recognition capabilities of mod- ern sensors are always limited to their direct field of view. In urban areas a lot of objects occlude important areas of in- terest. The information captured by another sensor from an- other perspective could solve such occluded situations. Fur- thermore, the capabilities to detect and classify various ob- jects in the surrounding can be improved by taking multiple views into account. In order to combine the data of two sensors into one co- ordinate system, a rigid transformation matrix has to be de- rived. The accuracy of modern e.g. satellite based relative pose estimation systems is not sufficient to guarantee a suit- able alignment. Therefore, a registration based approach is used in this work which aligns the captured environment data of two sensors from different positions. Thus their relative pose estimation obtained by traditional methods is improved and the data can be fused. To support this we present an approach which utilizes the uncertainty information of modern tracking systems to de- termine the possible field of view of the other sensor. Fur- thermore, it is estimated which parts of the captured data is directly visible to both, taking occlusion and shadowing ef- fects into account. Afterwards a registration method, based on the iterative closest point (ICP) algorithm, is applied to that data in order to get an accurate alignment. The contribution of the presented approch to the achiev- able accuracy is shown with the help of ground truth data from a LiDAR simulation within a 3-D crossroad model. Re- sults show that a two dimensional position and heading esti- mation is sufficient to initialize a successful 3-D registration process. Furthermore it is shown which initial spatial align- ment is necessary to obtain suitable registration results.
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Fåk, Joel, and Tomas Wilkinson. "Autonomous Mapping and Exploration of Dynamic Indoor Environments." Thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97609.
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This thesis describes all the necessary parts needed to build a complete system for autonomous indoor mapping in 3D. The robotic platform used is a two-wheeled Segway, operating in a planar environment. This, together with wheel odometers, an Inertial Measurement Unit (IMU), two Microsoft Kinects and a laptop comprise the backbone of the system, which can be divided into three parts: The localization and mapping part, which fundamentally is a SLAM (simultaneous localization and mapping) algorithm implemented using the registration technique Iterative Closest Point (ICP). Along with the map being in 3D, it also designed to handle the mapping of dynamic scenes, something absent from the standard SLAM design. The planning used by the system is twofold. First, the path planning - finding a path from the current position to a destination - and second, the target planning - determining where to go next given the current state of the map and the robot. The third part of the system is the control and collision systems, which while they have not received much focus, are very necessary for a fully autonomous system. Contributions made by this thesis include: The 3D map framework Octomap is extended to handle the mapping of dynamic scenes; A new method for target planning, based on image processing is presented; A calibration procedure for the robot is derived that gives a full six degree of freedom pose for each Kinect. Results show that our calibration procedure produces an accurate pose for each Kinect, which is crucial for a functioning system. The dynamic mapping is shown to outperform the standard occupancy grid in fundamental situations that arise when mapping dynamic scenes. Additionally, the results indicate that the target planning algorithm provides a fast and easy way to plan new target destinations. Finally, the entire system’s autonomous mapping capabilities are evaluated together, producing promising results. However, it also highlights some problems that limit the system’s performance such as the inaccuracy and short range of the Kinects or noise added and reinforced by the multiple subsystemsDetta exjobb beskriver delarna som krävs för att för bygga ett komplett system som autonomt kartlägger inomhusmiljöer i tre dimensioner. Robotplattformen är en Segway, som är kapabel att röra sig i ett plan. Segwayn, tillsammans med en tröghetssensor, två Microsoft Kinects och en bärbar dator utgör grunden till systemet, som kan delas i tre delar: En lokaliserings- och karteringsdel, som i grunden är en SLAM-algoritm (simultan lokalisering och kartläggning) baserad på registreringsmetoden Iterative Closest Point (ICP). Kartan som byggs upp är i tre dimensioner och ska dessutom hantera kartläggningen av dynamiska miljöer, något som orginalforumleringen av SLAM problemet inte klarar av. En automatisk planeringsdel, som består av två delar. Dels ruttplanering som går ut på att hitta en väg från sin nuvarande position till det valda målet och dels målplanering som innebär att välja ett mål att åka till givet den nuvarande kartan och robotens nuvarande position. Systemets tredje del är regler- och kollisionssystemen. Dessa system har inte varit i fokus i detta arbete, men de är ändå högst nödvändiga för att ett autonomt system skall fungera. Detta examensarbete bidrar med följande: Octomap, ett ramverk för kartläggningen i 3D, har utökats för att hantera kartläggningen av dynamiska miljöer; En ny metod för målplanering, baserad på bildbehandling läggs fram; En kalibreringsprocedur för roboten är framtagen som ger den fullständiga posen i förhållande till roboten för varje Kinect. Resultaten visar att vår kalibreringsprocedur ger en nogrann pose for för varje Kinect, vilket är avgörande för att systemet ska fungera. Metoden för kartläggningen av dynamiska miljöer visas prestera bra i grundläggande situationer som uppstår vid kartläggning av dynamiska miljöer. Vidare visas att målplaneringsalgoritmen ger ett snabbt och enkelt sätt att planera mål att åka till. Slutligen utvärderas hela systemets autonoma kartläggningsförmåga, som ger lovande resultat. Dock lyfter resultat även fram problem som begränsar systemets prestanda, till exempel Kinectens onoggranhet och korta räckvidd samt brus som läggs till och förstärks av de olika subsystemen.
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Smith, Michael. "Non-parametric workspace modelling for mobile robots using push broom lasers." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:50224eb9-73e8-4c8a-b8c5-18360d11e21b.
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This thesis is about the intelligent compression of large 3D point cloud datasets. The non-parametric method that we describe simultaneously generates a continuous representation of the workspace surfaces from discrete laser samples and decimates the dataset, retaining only locally salient samples. Our framework attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity. The work presented here has a specific focus on gathering and processing laser measurements taken from a moving platform in outdoor workspaces. We introduce a somewhat unusual parameterisation of the problem and look to Gaussian Processes as the fundamental machinery in our processing pipeline. Our system compresses laser data in a fashion that is naturally sympathetic to the underlying structure and complexity of the workspace. In geometrically complex areas, compression is lower than that in geometrically bland areas. We focus on this property in detail and it leads us well beyond a simple application of non-parametric techniques. Indeed, towards the end of the thesis we develop a non-stationary GP framework whereby our regression model adapts to the local workspace complexity. Throughout we construct our algorithms so that they may be efficiently implemented. In addition, we present a detailed analysis of the proposed system and investigate model parameters, metric errors and data compression rates. Finally, we note that this work is predicated on a substantial amount of robotics engineering which has allowed us to produce a high quality, peer reviewed, dataset - the first of its kind.
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Avdiu, Blerta. "Matching Feature Points in 3D World." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Data- och elektroteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-23049.
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This thesis work deals with the most actual topic in Computer Vision field which is scene understanding and this using matching of 3D feature point images. The objective is to make use of Saab’s latest breakthrough in extraction of 3D feature points, to identify the best alignment of at least two 3D feature point images. The thesis gives a theoretical overview of the latest algorithms used for feature detection, description and matching. The work continues with a brief description of the simultaneous localization and mapping (SLAM) technique, ending with a case study on evaluation of the newly developed software solution for SLAM, called slam6d. Slam6d is a tool that registers point clouds into a common coordinate system. It does an automatic high-accurate registration of the laser scans. In the case study the use of slam6d is extended in registering 3D feature point images extracted from a stereo camera and the results of registration are analyzed. In the case study we start with registration of one single 3D feature point image captured from stationary image sensor continuing with registration of multiple images following a trail. Finally the conclusion from the case study results is that slam6d can register non-laser scan extracted feature point images with high-accuracy in case of single image but it introduces some overlapping results in the case of multiple images following a trail.
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Su, Po-Chang. "A ROBUST RGB-D SLAM SYSTEM FOR 3D ENVIRONMENT WITH PLANAR SURFACES." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/17.
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Simultaneous localization and mapping is the technique to construct a 3D map of unknown environment. With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this thesis, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.
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Emord, Nicholas. "High Speed, Micron Precision Scanning Technology for 3D Printing Applications." UNF Digital Commons, 2018. https://digitalcommons.unf.edu/etd/821.
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Modern 3D printing technology is becoming a more viable option for use in industrial manufacturing. As the speed and precision of rapid prototyping technology improves, so too must the 3D scanning and verification technology. Current 3D scanning technology (such as CT Scanners) produce the resolution needed for micron precision inspection. However, the method lacks in speed. Some scans can be multiple gigabytes in size taking several minutes to acquire and process. Especially in high volume manufacturing of 3D printed parts, such delays prohibit the widespread adaptation of 3D scanning technology for quality control. The limiting factors of current technology boil down to computational and processing power along with available sensor resolution and operational frequency. Realizing a 3D scanning system that produces micron precision results within a single minute promises to revolutionize the quality control industry.
The specific 3D scanning method considered in this thesis utilizes a line profile triangulation sensor with high operational frequency, and a high-precision mechanical actuation apparatus for controlling the scan. By syncing the operational frequency of the sensor to the actuation velocity of the apparatus, a 3D point cloud is rapidly acquired. Processing of the data is then performed using MATLAB on contemporary computing hardware, which includes proper point cloud formatting and implementation of the Iterative Closest Point (ICP) algorithm for point cloud stitching. Theoretical and physical experiments are performed to demonstrate the validity of the method. The prototyped system is shown to produce multiple loosely-registered micron precision point clouds of a 3D printed object that are then stitched together to form a full point cloud representative of the original part. This prototype produces micron precision results in approximately 130 seconds, but the experiments illuminate upon the additional investments by which this time could be further reduced to approach the revolutionizing one-minute milestone.
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Schubert, Stefan. "Optimierter Einsatz eines 3D-Laserscanners zur Point-Cloud-basierten Kartierung und Lokalisierung im In- und Outdoorbereich." Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-161415.
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Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.
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Pospíšil, Aleš. "Detekce a sledování polohy hlavy v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218928.
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Diplomová práce je zaměřena na problematiku detekce a sledování polohy hlavy v obraze jako jednu s možností jak zlepšit možnosti interakce mezi počítačem a člověkem. Hlavním přínosem diplomové práce je využití inovativních hardwarových a softwarových technologií jakými jsou Microsoft Kinect, Point Cloud Library a CImg Library. Na úvod je představeno shrnutí předchozích prací na podobné téma. Následuje charakteristika a popis databáze, která byla vytvořena pro účely diplomové práce. Vyvinutý systém pro detekci a sledování polohy hlavy je založený na akvizici 3D obrazových dat a registračním algoritmu Iterative Closest Point. V závěru diplomové práce je nabídnuto hodnocení vzniklého systému a jsou navrženy možnosti jeho budoucího zlepšení.
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Pooja, A. "A Multiview Extension Of The ICP Algorithm." Thesis, 2010. http://etd.iisc.ernet.in/handle/2005/1284.
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The Iterative Closest Point (ICP) algorithm has been an extremely popular method for 3D points or surface registration. Given two point sets, it simultaneously solves for correspondences and estimates the motion between these two point sets. However, by only registering two such views at a time, ICP fails to exploit the redundant information available in multiple views that have overlapping regions. In this thesis, a multiview extension of the ICP algorithm is provided that simultaneously averages the redundant information available in the views with overlapping regions. Variants of this method that carry out such simultaneous registration in a causal manner and that utilize the transitivity property of point correspondences are also provided. The improved accuracy in registration of these motion averaged approaches in comparison with the conventional ICP method is established through extensive experiments. In addition, the motion averaged approaches are compared with the existing multiview techniques of Bergevin et. al. and Benjemaa et. al. The results of the methods applied to the Happy Buddha and the Stanford Bunny datasets of 3D Stanford repository and to the Pooh and the Bunny datasets of the Ohio (MSU/WSU) Range Image database are also presented.
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Wang, Jia-An, and 王家安. "Application of Iterative Closest Point Algorithm for SLAM." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/96880440748270066274.
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碩士國立臺灣海洋大學
通訊與導航工程學系
101
Abstract This study uses a wheeled mobile robot to explore unknown indoor environment and build up a map of the unknown environment. The robot utilizes laser measurement sensor with a indoor localization system to detect obstacles and identify unknown environment. The localization system provides the position of the robot and is used for map comparison. Fuzzy theory is applied to control design. The proposed control scheme can control the wheeled mobile robot move along walls and avoid obstacles. The controller and Iterative Closest Point (ICP) algorithm are coded by MATLAB. Hardware implementation uses LABVIEW 8.6 to realize interface between human and machine. The Iterative Closest Point and the LMS data are applied to obtain rotation angle, displacement and path trajectory. In order to reduce the computation time and achieve real-time positioning, the KD-tree is used. By calculating the rotation and translation matrices among different data point sets, distance and angle information of the moving robot can be recorded. And use less number of reading cycle can reduce number of alignment and accumulated error. Worst point rejection method can delete less corresponded points which makes ICP process converge to a local optimum. The track of the robot can be obtained. With sensed data of obstructions and walls, a map of unknown environment can be generated by curve fitting methods. The proposed control scheme is tested in four different terrains. According to preset moving strategy, the mobile robot can automatically stop when it approaches the origin of the coordinates, then the environmental exploration is completed. The experiments confirm that the simultaneous localization and mapping (SLAM) control scheme works properly.
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Lu, Ze-Kang, and 呂則慷. "Structure from Motion of a polycamera by Iterative Closest Point." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3p7xgc.
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碩士國立臺灣大學
資訊工程學研究所
105
About Structure from Motion, we can see many applications. In traditional Structure from Motion, we use single-camera to do it, in the beginning, take picture at position A, and get the 2D image, then take picture at position B, and also get the 2D image, Structure from Motion is that we use these 2D images which are taken from different positions to reconstruct the 3D structure, this is one of the application. Another application is that when we move our camera to different positions, there is a transformation matrix between two cameras of positions, if we can use computer to calculate the transformation matrix between the two position, we can easily and correctly move the camera, and then reconstruct the camera’s track, so that we can do another application such as digital tourism guide, if we have a lot of 2D images which are taken from different positions, then we can move the camera to different places so that we can see different 2D images at different positions. Out paper is that we hope to get the transformation matrix, and then reconstruct the camera’s track, but up to now we almost use single-camera to achieve it, we hope to use different kinds of camera to achieve it, so we adopt a polycamera which has four fisheye cameras, and they are perpendicular to each other, but when we use polycamera, there will be a problem that the traditional single-camera doesn’t exist, if two cameras of positions rotate almost 45 degrees, it lead to the problem that at position A, the fisheye 1 can take one point x, but at position B, the fisheye 1 may not take that point x, so that when we try to reconstruct the camera’s moving track, the error rate will increase largely, so we think out another method ,the method is that at every position, we use four 2D images at every position to calculate their feature points, and then transform these 2D feature points to 3D point cloud, so that there are 3D point clouds at every position, next we use Iterative Closest Point algorithm to calculate the transformation matrix between two 3D point clouds, we hope that we can get a best transformation matrix so as to correctly reconstruct to camera’s moving track. We devote to use nontraditional method, such as single-camera, to achieve, we use polycamera with four fisheye cameras to reconstruct the camera’s moving track, we hope that we can reconstruct not just a small zone, and we can reconstruct the 360 panaroma in response to the trend of virtual reality.
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鄭景懋. "The Improvement of Iterative Closest Point Algorithm for Environmental Map Building." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/42137690348326475825.
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Morency, Louis-Philippe. "Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint." 2003. http://hdl.handle.net/1721.1/7102.
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In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.
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CHANG, Hua-En, and 張華恩. "Map Building of Unknown Environment Using PSO-Tuned Enhanced Iterative Closest Point Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/10378631354597018761.
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碩士國立臺灣師範大學
應用電子科技研究所
101
This paper proposes a PSO-tuned enhanced iterative closest point algorithm (ICP) to build maps for an unknown environment using a Pioneer 3-DX wheeled mobile robot with a laser measure scanner (LMS-100). The proposed algorithm first aligns each scanned information by the ICP algorithm. Because traditional ICP algorithms are easily affected by noise and outliers, false matching points and alignment errors are therefore inevitable. As a result, there are more and more errors accumulated as the scanning process by the laser scanner continues, which results in imperfect alignment of the environmental map as a whole. Therefore, this paper proposes the use of Particle Swarm Optimization (PSO) to work with the Enhanced-ICP in order to effectively filter out outliers and avoid false matching points during the map building process, where PSO is used to align two data sets to avoid huge transformation that causes local optima. Then, we use part of global map as the reference data set with overlapping points for subsequent data matching. The proposed algorithm not only improves outlier and noise problem but also reduces false matching points so that it has better alignment and smaller accumulated errors. As a result, good environmental map is obtained.
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LI, Tse-ting, and 李則霆. "Geometric measure of 3D model surface deformation based on the iterative closest point algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/55374020945619254211.
Full textAbstract:
碩士國立臺灣科技大學
材料科學與工程系
102
With 3D modeling technologies, we can build virtual objects (point clouds), such as mountains, buildings, and faces. These 3D virtual objects help us obtain more information than traditional 2D images. In 2D cases, we can easily evaluate the similarity measure between two images. However, it is not easy for us to do so between two 3D virtual objects, especially when they are obtained from different devices (different coordinate systems). Iterative Closest Point (ICP) provides a way to find corresponding points between two point cloud data. It has been applied to many fields, such as object alignment, face and hand recognition, camera tracking, etc. However, it does not provide a geometric measure between two models for deformation evaluation. In this thesis, we propose a geometric measure method based on ICP. This measure is used to evaluate the degree of deformation. From experimental results, the proposed method can detect the local deformation area and give a quantitative evaluation between the original object and deformed one.
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Wen, Yi-Jou, and 溫苡柔. "Cloud-Computing Based Multi-Robot Map Building of Uncertain Environment Based on Enhanced Iterative Closest Point Algorithm." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/edtf55.
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Yin, Ming-Tzuoo, and 鄞銘佐. "Robot Pose Estimation Using Laser Range Finder with Iterative Closest Point Algorithm in an Unknown Stationary Environment." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/56448354772096071934.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
98
Localization is an important issue for robot navigation in an unknown stationary environment. ICP (Iterative Closest Point) algorithm can be used to solve problem of curve registration by the concept of least square errors. It is a useful tool for mapping matching, localization, and map building because of its capability to eliminate systematic and nonsystematic errors of robot simultaneously. However, differences in two mappings and restrictions from laser range finder make complete mapping matching impossible in actual situation. In this thesis, a method is proposed to find common parts of the original mappings called hybrid potential common parts before using ICP algorithm. Then, there are three methods to promote accuracy and robustness of overall algorithm. The experimental difficulties in reality are mentioned in addition. The goal of above discussed methods is to assist ICP algorithm to match maps more successfully and overcome different types of obstacles in an indoor stationary environment.
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Ravishankar, S. "Robust Registration of Measured Point Set for Computer-Aided Inspection." Thesis, 2013. http://etd.iisc.ernet.in/2005/3297.
Full textAbstract:
This thesis addresses the problem of registering one point set with respect to
another. This problem arises in the context of the use of CMM/Scanners to inspect
objects especially with freeform surfaces. The tolerance verification process now
requires the comparison of measured points with the nominal geometry. This entails placement of the measured point set in the same reference frame as the nominal model. This problem is referred to as the registration or localization problem. In the most general form the tolerance verification task involves registering multiple point sets corresponding to multi-step scan of an object with respect to the nominal CAD model. This problem is addressed in three phases.
This thesis presents a novel approach to automated inspection by matching
point sets based on the Iterative Closest Point (ICP) algorithm. The Modified ICP
(MICP) algorithm presented in the thesis improves upon the existing methods through the use of a localized region based triangulation technique to obtain correspondences for all the inspection points and achieves dramatic reduction in computational effort. The use of point sets to represent the nominal surface and shapes enables handling different systems and formats. Next, the thesis addresses the important problem of establishing registration between point sets in different reference frames when the initial relative pose between them is significantly large. A novel initial pose invariant methodology has been developed. Finally, the above approach is extended to registration of multiview inspection data sets based on acquisition of transformation information of each inspection view using the virtual gauging concept. This thesis describes implementation to address each of these problems in the area of automated registration and verification leading towards automatic inspection.
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Schubert, Stefan. "Optimierter Einsatz eines 3D-Laserscanners zur Point-Cloud-basierten Kartierung und Lokalisierung im In- und Outdoorbereich." Master's thesis, 2014. https://monarch.qucosa.de/id/qucosa%3A20206.
Full textAbstract:
Die Kartierung und Lokalisierung eines mobilen Roboters in seiner Umgebung ist eine wichtige Voraussetzung für dessen Autonomie. In dieser Arbeit wird der Einsatz eines 3D-Laserscanners zur Erfüllung dieser Aufgaben untersucht. Durch die optimierte Anordnung eines rotierenden 2D-Laserscanners werden hochauflösende Bereiche vorgegeben. Zudem wird mit Hilfe von ICP die Kartierung und Lokalisierung im Stillstand durchgeführt. Bei der Betrachtung zur Verbesserung der Bewegungsschätzung wird auch eine Möglichkeit zur Lokalisierung während der Bewegung mit 3D-Scans vorgestellt. Die vorgestellten Algorithmen werden durch Experimente mit realer Hardware evaluiert.
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Mohammadzade, Narges Hoda. "Two- and Three-dimensional Face Recognition under Expression Variation." Thesis, 2012. http://hdl.handle.net/1807/32773.
Full textAbstract:
In this thesis, the expression variation problem in two-dimensional (2D) and three-dimensional (3D) face recognition is tackled. While discriminant analysis (DA) methods are effective solutions for recognizing expression-variant 2D face images, they are not directly applicable when only a single sample image per subject is available. This problem is addressed in this thesis by introducing expression subspaces which can be used for synthesizing new expression images from subjects with only one sample image. It is proposed that by augmenting a generic training set with the gallery and their synthesized new expression images, and then training DA methods using this new set, the face recognition performance can be significantly improved. An important advantage of the proposed method is its simplicity; the expression of an image is transformed simply by projecting it into another subspace. The above proposed solution can also be used in general pattern recognition applications.
The above method can also be used in 3D face recognition where expression variation is a more serious issue. However, DA methods cannot be readily applied to 3D faces because of the lack of a proper alignment method for 3D faces. To solve this issue, a method is proposed for sampling the points of the face that correspond to the same facial features across all faces, denoted as the closest-normal points (CNPs). It is shown that the performance of the linear discriminant analysis (LDA) method, applied to such an aligned representation of 3D faces, is significantly better than the performance of the state-of-the-art methods which, rely on one-by-one registration of the probe faces to every gallery face. Furthermore, as an important finding, it is shown that the surface normal vectors of the face provide a higher level of discriminatory information rather than the coordinates of the points.
In addition, the expression subspace approach is used for the recognition of 3D faces from single sample. By constructing expression subspaces from the surface normal vectors at the CNPs, the surface normal vectors of a 3D face with single sample can be synthesized under other expressions. As a result, by improving the estimation of the within-class scatter matrix using the synthesized samples, a significant improvement in the recognition performance is achieved.