Дисертації з теми "Mobile robot mapping"
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HASSANZADEH, Aidin. "Mobile Robot Wind Mapping." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-34606.
Повний текст джерелаWong, Chee Kit. "Cognitive inspired mapping by an autonomous mobile robot." Click here to access this resource online, 2008. http://hdl.handle.net/10292/427.
Повний текст джерелаWANG, XUAN. "2D Mapping Solutionsfor Low Cost Mobile Robot." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138427.
Повний текст джерелаNordin, Peter. "Mobile Robot Traversability Mapping : For Outdoor Navigation." Licentiate thesis, Linköpings universitet, Fluida och mekatroniska system, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85937.
Повний текст джерелаDeďo, Michal. "Řízení čtyřkolového mobilního robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229688.
Повний текст джерелаMcCoig, Kenneth. "A MOBILE ROBOTIC COMPUTING PLATFORM FOR THREE-DIMENSIONAL INDOOR MAPPI." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2372.
Повний текст джерелаM.S.Cp.E.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Computer Engineering
Ezequiel, Carlos Favis. "Real-Time Map Manipulation for Mobile Robot Navigation." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4481.
Повний текст джерелаCasalaro, Giuseppina Lucia, and Giulio Cattivera. "MODEL-DRIVEN ENGINEERING FOR MOBILE ROBOT SYSTEMS: A SYSTEMATIC MAPPING STUDY." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28261.
Повний текст джерелаWilliams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." University of Sydney. Aerospace, Mechanical and Mechatronic Engineering, 2002. http://hdl.handle.net/2123/809.
Повний текст джерелаSezginalp, Emre. "Simultaneous Localization And Mapping For A Mobile Robot Operating In Outdoor Environments." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609191/index.pdf.
Повний текст джерелаMeger, David Paul. "Planning, localization, and mapping for a mobile robot in a camera network." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101623.
Повний текст джерелаReggente, Matteo. "Statistical gas distribution modelling for mobile robot applications." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37896.
Повний текст джерелаMatsumoto, Takeshi, and takeshi matsumoto@flinders edu au. "Real-Time Multi-Sensor Localisation and Mapping Algorithms for Mobile Robots." Flinders University. Computer Science, Engineering and Mathematics, 2010. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20100302.131127.
Повний текст джерелаBacca, Cortés Eval Bladimir. "Appearance-based mapping and localization using feature stability histograms for mobile robot navigation." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/83589.
Повний текст джерелаEste trabajo propone un método de SLAM basado en apariencia cuya principal contribución es el Histograma de Estabilidad de Características (FSH). El FSH es construido por votación, si una característica es re-observada, ésta será promovida; de lo contrario su valor FSH progresivamente es reducido. El FSH es basado en el modelo de memoria humana para ocuparse de ambientes cambiantes y SLAM a largo término. Este modelo introduce conceptos como memoria a corto plazo (STM) y largo plazo (LTM), las cuales retienen información por cortos y largos periodos de tiempo. Si una entrada a la STM es reforzada, ésta hará parte de la LTM (i.e. es más estable). Sin embargo, este trabajo propone un modelo de memoria diferente, permitiendo a cualquier entrada ser parte de la STM o LTM considerando su intensidad. Las características más estables son solamente usadas en SLAM. Esta innovadora estrategia de manejo de características es capaz de hacer frente a ambientes cambiantes y SLAM de largo término.
Easton, Adam. "From mobile robot localisation to multi-robot exploration : a Gaussian approach to localisation and mapping in large environments." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442398.
Повний текст джерелаWijk, Olle. "Triangulation Based Fusion of Sonar Data with Application in Mobile Robot Mapping and Localization." Doctoral thesis, Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3124.
Повний текст джерелаDogruer, Can Ulas. "Global Urban Localization Of An Outdoor Mobile Robot Using Satellite Images." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610446/index.pdf.
Повний текст джерелаmapping and localization on the acquired map. First the images of the outdoor environment is downloaded from a website such as Google Earth and then these images are processed by utilizing several artificial neural network topologies to create maps. Once these maps are obtained, the localization is done by using Monte Carlo localization. This dissertation addresses a solution for the information which is most of the time taken for granted in most studies
a prior map of environment. Mapping is solved by using a novel approach
the map of the environment is created by processing satellite images. Several global localization techniques are developed and evaluated to be used with these map so as to localize a mobile robot globally. The outcome of this novel approach presented here may serve as a virtual GPS. Mobile phone applications can localize a user within a circle of uncertainty without GPS. This crude localization may be used to download relevant satellite images of the local environment. Once the mobile robot is localized on the map created from the satellite images by using available techniques in the literature i.e. Monte Carlo localization, it may be claimed that it is localized on Earth.
Althaus, Philipp. "Indoor Navigation for Mobile Robots : Control and Representations." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3644.
Повний текст джерелаThis thesis deals with various aspects of indoor navigationfor mobile robots. For a system that moves around in ahousehold or office environment,two major problems must betackled. First, an appropriate control scheme has to bedesigned in order to navigate the platform. Second, the form ofrepresentations of the environment must be chosen.
Behaviour based approaches have become the dominantmethodologies for designing control schemes for robotnavigation. One of them is the dynamical systems approach,which is based on the mathematical theory of nonlineardynamics. It provides a sound theoretical framework for bothbehaviour design and behaviour coordination. In the workpresented in this thesis, the approach has been used for thefirst time to construct a navigation system for realistic tasksin large-scale real-world environments. In particular, thecoordination scheme was exploited in order to combinecontinuous sensory signals and discrete events for decisionmaking processes. In addition, this coordination frameworkassures a continuous control signal at all times and permitsthe robot to deal with unexpected events.
In order to act in the real world, the control system makesuse of representations of the environment. On the one hand,local geometrical representations parameterise the behaviours.On the other hand, context information and a predefined worldmodel enable the coordination scheme to switchbetweensubtasks. These representations constitute symbols, on thebasis of which the system makes decisions. These symbols mustbe anchored in the real world, requiring the capability ofrelating to sensory data. A general framework for theseanchoring processes in hybrid deliberative architectures isproposed. A distinction of anchoring on two different levels ofabstraction reduces the complexity of the problemsignificantly.
A topological map was chosen as a world model. Through theadvanced behaviour coordination system and a proper choice ofrepresentations,the complexity of this map can be kept at aminimum. This allows the development of simple algorithms forautomatic map acquisition. When the robot is guided through theenvironment, it creates such a map of the area online. Theresulting map is precise enough for subsequent use innavigation.
In addition, initial studies on navigation in human-robotinteraction tasks are presented. These kinds of tasks posedifferent constraints on a robotic system than, for example,delivery missions. It is shown that the methods developed inthis thesis can easily be applied to interactive navigation.Results show a personal robot maintaining formations with agroup of persons during social interaction.
Keywords:mobile robots, robot navigation, indoornavigation, behaviour based robotics, hybrid deliberativesystems, dynamical systems approach, topological maps, symbolanchoring, autonomous mapping, human-robot interaction
HERRERA, LUIS ERNESTO YNOQUIO. "MOBILE ROBOT SIMULTANEOUS LOCALIZATION AND MAPPING USING DP-SLAM WITH A SINGLE LASER RANGE FINDER." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2011. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=34617@1.
Повний текст джерелаCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
SLAM (Mapeamento e Localização Simultânea) é uma das áreas mais pesquisadas na Robótica móvel. Trata-se do problema, num robô móvel, de construir um mapa sem conhecimento prévio do ambiente e ao mesmo tempo manter a sua localização nele. Embora a tecnologia ofereça sensores cada vez mais precisos, pequenos erros na medição são acumulados comprometendo a precisão na localização, sendo estes evidentes quando o robô retorna a uma posição inicial depois de percorrer um longo caminho. Assim, para melhoria do desempenho do SLAM é necessário representar a sua formulação usando teoria das probabilidades. O SLAM com Filtro Extendido de Kalman (EKF-SLAM) é uma solução básica, e apesar de suas limitações é a técnica mais popular. O Fast SLAM, por outro lado, resolve algumas limitações do EKF-SLAM usando uma instância do filtro de partículas conhecida como Rao-Blackwellized. Outra solução bem sucedida é o DP-SLAM, o qual usa uma representação do mapa em forma de grade de ocupação, com um algoritmo hierárquico que constrói mapas 2D bastante precisos. Todos estes algoritmos usam informação de dois tipos de sensores: odômetros e sensores de distância. O Laser Range Finder (LRF) é um medidor laser de distância por varredura, e pela sua precisão é bastante usado na correção do erro em odômetros. Este trabalho apresenta uma detalhada implementação destas três soluções para o SLAM, focalizado em ambientes fechados e estruturados. Apresenta-se a construção de mapas 2D e 3D em terrenos planos tais como em aplicações típicas de ambientes fechados. A representação dos mapas 2D é feita na forma de grade de ocupação. Por outro lado, a representação dos mapas 3D é feita na forma de nuvem de pontos ao invés de grade, para reduzir o custo computacional. É considerado um robô móvel equipado com apenas um LRF, sem nenhuma informação de odometria. O alinhamento entre varreduras laser é otimizado fazendo o uso de Algoritmos Genéticos. Assim, podem-se construir mapas e ao mesmo tempo localizar o robô sem necessidade de odômetros ou outros sensores. Um simulador em Matlab é implementado para a geração de varreduras virtuais de um LRF em um ambiente 3D (virtual). A metodologia proposta é validada com os dados simulados, assim como com dados experimentais obtidos da literatura, demonstrando a possibilidade de construção de mapas 3D com apenas um sensor LRF.
Simultaneous Localization and Mapping (SLAM) is one of the most widely researched areas of Robotics. It addresses the mobile robot problem of generating a map without prior knowledge of the environment, while keeping track of its position. Although technology offers increasingly accurate position sensors, even small measurement errors can accumulate and compromise the localization accuracy. This becomes evident when programming a robot to return to its original position after traveling a long distance, based only on its sensor readings. Thus, to improve SLAM s performance it is necessary to represent its formulation using probability theory. The Extended Kalman Filter SLAM (EKF-SLAM) is a basic solution and, despite its shortcomings, it is by far the most popular technique. Fast SLAM, on the other hand, solves some limitations of the EKFSLAM using an instance of the Rao-Blackwellized particle filter. Another successful solution is to use the DP-SLAM approach, which uses a grid representation and a hierarchical algorithm to build accurate 2D maps. All SLAM solutions require two types of sensor information: odometry and range measurement. Laser Range Finders (LRF) are popular range measurement sensors and, because of their accuracy, are well suited for odometry error correction. Furthermore, the odometer may even be eliminated from the system if multiple consecutive LRF scans are matched. This works presents a detailed implementation of these three SLAM solutions, focused on structured indoor environments. The implementation is able to map 2D environments, as well as 3D environments with planar terrain, such as in a typical indoor application. The 2D application is able to automatically generate a stochastic grid map. On the other hand, the 3D problem uses a point cloud representation of the map, instead of a 3D grid, to reduce the SLAM computational effort. The considered mobile robot only uses a single LRF, without any odometry information. A Genetic Algorithm is presented to optimize the matching of LRF scans taken at different instants. Such matching is able not only to map the environment but also localize the robot, without the need for odometers or other sensors. A simulation program is implemented in Matlab to generate virtual LRF readings of a mobile robot in a 3D environment. Both simulated readings and experimental data from the literature are independently used to validate the proposed methodology, automatically generating 3D maps using just a single LRF.
Bore, Nils. "Object Instance Detection and Dynamics Modeling in a Long-Term Mobile Robot Context." Doctoral thesis, KTH, Robotik, perception och lärande, RPL, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219813.
Повний текст джерелаUnder de senaste åren har enklare service-robotar, såsom autonoma dammsugare och gräsklippare, börjat säljas, och blivit alltmer vanliga. Nästa generations service-robotar förväntas utföra mer komplexa uppgifter, till exempel att städa upp utspridda föremål i ett vardagsrum. För att uppnå detta måste robotarna kunna navigera i ostrukturerade miljöer, och förstå hur de kan bringas i ordning. I denna avhandling undersöker vi abstrakta representationer som kan förverkliga generalla städrobotar, samt robotar som kan hämta föremål. Vi diskuterar vad dessa specifika tillämpningar kräver i form av representationer, och argumenterar för att en lösning på dessa problem vore mer generellt applicerbar på grund av uppgifternas föremåls-centrerade natur. Vi närmar oss uppgiften genom två viktiga insikter. Till att börja medär många av dagens robot-representationer begränsade till rumsdomänen. De utelämnar alltså att modellera den variation som sker över tiden, och utnyttjar därför inte att rörelsen som kan ske under en given tidsperiod är begränsad. Vi argumenterar för att det är kritiskt att också inkorperara miljöns rörelse i robotens modell. Genom att modellera omgivningen på en föremåls-nivå möjliggörs tillämpningar som städning och hämtning av rörliga objekt. Den andra insikten kommer från att mobila robotar nu börjar bli så robusta att de kan patrullera i en och samma omgivning under flera månader. Dekan därför samla in stora mängder data från enskilda omgivningar. Med dessa stora datamängder börjar det bli möjligt att tillämpa så kallade "unsupervised learning"-metoder för att lära sig modeller av enskilda miljöer utan mänsklig inblandning. Detta tillåter robotarna att anpassa sig till förändringar i omgivningen, samt att lära sig koncept som kan vara svåra att förutse på förhand. Vi ser detta som en grundläggande förmåga hos en helt autonom robot. Kombinationen av unsupervised learning och modellering av omgivningens dynamik är intressant. Eftersom dynamiken varierar mellan olika omgivningar,och mellan olika objekt, kan learning hjälpa oss att fånga dessa variationer,och skapa mer precisa dynamik-modeller. Något som försvårar modelleringen av omgivningens dynamik är att roboten inte kan observera hela omgivningen på samma gång. Detta betyder att saker kan flyttas långa sträckor mellan två observationer. Vi visar hur man kan adressera detta i modellen genom att inlemma flera olika sätt som ett föremål kan flyttas på. Det resulterande systemet är helt probabilistiskt, och kan hålla reda på samtliga föremål i robotens omgivning. Vi demonstrerar även metoder för att upptäcka och lära sig föremål i den statiska delen av omgivningen. Med det kombinerade systemet kan vi således representera och lära oss många aspekter av robotens omgivning. Genom experiment i mänskliga miljöer visar vi att systemet kan hålla reda på olika sorters föremål i stora, och dynamiska, miljöer.
QC 20171213
Botterill, Tom. "Visual navigation for mobile robots using the Bag-of-Words algorithm." Thesis, University of Canterbury. Computer Science and Software Engineering, 2011. http://hdl.handle.net/10092/5511.
Повний текст джерелаMalartre, Florent. "Perception intelligente pour la navigation rapide de robots mobiles en environnement naturel." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2011. http://tel.archives-ouvertes.fr/tel-00673435.
Повний текст джерелаSola, Ortega Joan. "Towards visual localization, mapping and moving objects tracking by a mobile robot: a geometric and probabilistic approach." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2007. http://tel.archives-ouvertes.fr/tel-00136307.
Повний текст джерелаSolà, Ortega Joan. "Towards visual localization, mapping and moving objects tracking by a mobile robot : a geometric and probabilistic approach." Toulouse, INPT, 2007. http://ethesis.inp-toulouse.fr/archive/00000528/.
Повний текст джерелаIn this thesis we solve the problem of simultaneously reconstructing a representation of the world geometry, the observer trajectory, and the moving objects trajectories, with the aid of vision. We proceed by dividing the problem into three steps. First, we give a solution to the Simultaneous Localization And Mapping problem (SLAM) for monocular vision that is able to adequately perform in the most ill-conditioned situations : those where the observer approaches the scene in straight line. Second, we incorporate instantaneous 3D observability by duplicating vision hardware with monocular algorithms. This eliminates inherent drawbacks of classic stereo systems. Third, we add detection and tracking of moving objects by making use of this full 3D observability. We choose a sparse, punctual representation of both the world and the moving objects. The computational payload of the perception algorithms is alleviated focusing the attention to those image regions with the highest interest
Solà, Ortega Joan Devy Michel Monin André. "Towards visual localization, mapping and moving objects tracking by a mobile robot a geometric and probabilistic approach /." Toulouse : INP Toulouse, 2007. http://ethesis.inp-toulouse.fr/archive/00000528.
Повний текст джерелаEl, Hamzaoui Oussama. "Localisation et cartographie simultanées pour un robot mobile équipé d'un laser à balayage : CoreSLAM." Phd thesis, Ecole Nationale Supérieure des Mines de Paris, 2012. http://pastel.archives-ouvertes.fr/pastel-00935600.
Повний текст джерелаWilliams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." Thesis, The University of Sydney, 2001. http://hdl.handle.net/2123/809.
Повний текст джерелаPersson, Martin. "Semantic Mapping using Virtual Sensors and Fusion of Aerial Images with Sensor Data from a Ground Vehicle." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2186.
Повний текст джерелаBurian, František. "Tvorba multispektrálních map v mobilní robotice." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233689.
Повний текст джерелаOzgur, Ayhan. "A Novel Mobile Robot Navigation Method Based On Combined Feature Based Scan Matching And Fastslam Algorithm." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612431/index.pdf.
Повний текст джерелаWerner, Felix. "Vision-based topological mapping and localisation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/31815/1/Felix_Werner_Thesis.pdf.
Повний текст джерелаMachado, Karla Fedrizzi. "Módulo de auto-localização para um agente exploratório usando Filtro de Kalman." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2003. http://hdl.handle.net/10183/26953.
Повний текст джерелаBuild a robot capable of performing tasks without any human interference is one of the biggest challenges of the Mobile Robotics. Having only sensors, an autonomous robot needs to explore unknown environments and, simultaneously, build a reliable map in order to get its own location and perform the task. In the presence of odometry errors, the robot is not capable of establish its own position on its internal map and ends up building a deformed map that does not reflect reality. This paper presents a solution for the problem of self-localization of autonomous mobile robots. This solution uses a linear method for calculating estimatives called Kalman Filter to correct the robot's position on its internal mapping of the environment while exploring. The proposal considers that any being that moves in an environment always counts on having some reference points to establish its own position. This points are implemented as special objects called Kalman landmarks. In simulation, the recognition of such landmarks can be done in two different ways: through its position on the map or through its identity. In the experiments performed in simulations, the method is tested for different errors in the robot's inclination angle. The results are compared considering the deformations on the generated map, with and without the Kalman landmarks, and the average error of the robot's position during the exploratory process.
Rogers, John Gilbert. "Life-long mapping of objects and places in domestic environments." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47736.
Повний текст джерелаHähnel, Dirk. "Mapping with mobile robots." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974035599.
Повний текст джерелаDiaz, Espinosa Carlos Andrés. "Uma aplicação de navegação robótica autônoma através de visão computacional estéreo." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/263062.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-16T16:41:02Z (GMT). No. of bitstreams: 1 DiazEspinosa_CarlosAndres_M.pdf: 5130242 bytes, checksum: 334f37aa82bbde2c9ddbfe192baa7c48 (MD5) Previous issue date: 2010
Resumo: O presente trabalho descreve uma técnica de navegação autônoma, utilizando imagens estereoscópicas de câmeras para estimar o movimento de um robô em um ambiente desconhecido. Um método de correlação de pontos em imagens unidimensionais é desenvolvido para a identificação de pontos homólogos de duas imagens em uma cena. Utilizam-se métodos de segmentação de bordas ou contornos para extrair as principais características inerentes nas imagens. Constrói-se um mapa de profundidade dos pontos da imagem com maior similitude dentre os objetos visíveis no ambiente, utilizando um processo de triangulação. Finalmente a estimação do movimento bidimensional do robô é calculada aproveitando a relação epipolar entre dois ou mais pontos em pares de imagens. Experimentos realizados em ambientes virtuais e testes práticos verificam a viabilidade e robustez dos métodos em aplicações de navegação robótica
Abstract: The present work describes a technique for autonomous navigation using stereoscopic camera images to estimate the movement of a robot in an unknown environment. A onedimensional image point correlation method is developed for the identification of similar image points of a scene. Boundary or contour segments are used to extract the principal characteristics of the images. A depth map is built for the points with grater similarity, among the scene objects depicted, using a triangulation process. Finally, the bi-dimensional movement of a robot is estimated through epipolar relations between two or more correlated points in pairs of images. Virtual ambient and practical robot tests are preformed to evaluate the viability of employment and robustness of the proposed techniques
Mestrado
Mecanica dos Sólidos e Projeto Mecanico
Mestre em Engenharia Mecânica
Pronobis, Andrzej. "Semantic Mapping with Mobile Robots." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-34171.
Повний текст джерелаQC 20110527
Huang, Henry. "Bearing-only SLAM : a vision-based navigation system for autonomous robots." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/28599/1/Henry_Huang_Thesis.pdf.
Повний текст джерелаHuang, Henry. "Bearing-only SLAM : a vision-based navigation system for autonomous robots." Queensland University of Technology, 2008. http://eprints.qut.edu.au/28599/.
Повний текст джерелаKaess, Michael. "Incremental smoothing and mapping." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26572.
Повний текст джерелаCommittee Chair: Dellaert, Frank; Committee Member: Bobick, Aaron; Committee Member: Christensen, Henrik; Committee Member: Leonard, John; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Li, Hongjun. "Mapping in uncertain environments for mobile robots." Doctoral thesis, Universidade de Évora, 2019. http://hdl.handle.net/10174/26154.
Повний текст джерелаRyde, Julian. "Cooperative 3D Mapping and Localisation of Multiple Mobile Robots." Thesis, University of Essex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486562.
Повний текст джерелаГузик, Петро Євгенович, та Petro Huzyk. "Розробка та дослідження системи для побудови карти з одночасним контролем наявного місцерозташування і пройденого шляху в мобільних автономних засобах". Master's thesis, Тернопіль, ТНТУ, 2020. http://elartu.tntu.edu.ua/handle/lib/33425.
Повний текст джерелаУ роботі було розроблено автоматизовану систему для побудови карти з одночасним контролем наявного місцерозташування і пройденого шляху в мобільних автономних засобах. Було розроблено структурну та функціональну схему системи автоматизації. Розроблено функціонал дистанційного керування в ручному режимі використовуючи програмне забезпечення на ПК. Мобільний засіб оснащено камерою і платою-комп’ютером Raspberry Pi 3, драйвером двигуна постійного струму L293d та чотирма двигунами постійного струму. Відеопотік з відеокамери поступає на міні-комп’ютер Raspberry Pi 3, де кожен кадр обробляється, співставляється з попередніми кадрами і будується карта місцевості за допомогою виявлених ознак в кожному кадрі відеопотоку. In the work the automated system for construction of a map with simultaneous control of the available location and the passed way in mobile autonomous means was developed. The structural and functional scheme of the automation system was developed. Developed the functionality of remote control in manual mode using software on a PC. The mobile device is equipped with a camera and a Raspberry Pi 3 computer board, an L293d DC motor driver and four DC motors. The video stream from the camcorder is fed to the Raspberry Pi 3 mini-computer, where each frame is processed, mapped to previous frames, and a terrain map is built using the features detected in each frame of the video stream.
ЗМІСТ ВСТУП 3 1. АНАЛІТИЧНА ЧАСТИНА 4 1.1. Походження проблеми SLAM 4 1.2. Аналіз алгоритмів монокулярного SLAM 8 1.3. Сучасні та альтернативні підходи до вирішення проблеми SLAM 11 1.4. Аналіз реалізацій SLAM алгоритмів 13 2. НАУКОВО-ДОСЛІДНА ЧАСТИНА 16 2.1. Рекурсивне Баєсове оцінювання 16 2.1.1. Рекурсивна оцінка 16 2.1.2. Баєсова оцінка 17 2.2. Представлення просторової карти і стану системи 19 2.3. Баєсова фільтрація 22 2.4. Фільтр Калмана 24 2.5. Розширений фільтр Калмана 26 2.6. Корпускулярний фільтр 27 3. ТЕХНОЛОГІЧНА ЧАСТИНА 30 3.1. Опис конструкції прототипу 30 3.2. Програмна реалізація SLAM алгоритму 33 4. КОНСТРУКТОРСЬКА ЧАСТИНА 37 4.1. Структурні елементи SLAM та симуляція 37 4.1.1. Симуляція карти 39 4.1.2. Симуляція давачів одометрії 42 4.1.3. Симуляція енкодерів 45 4.1.4. Симуляція LiDAR 50 5. СПЕЦІАЛЬНА ЧАСТИНА 55 5.1. Вибір двигунів і енкодерів 55 5.2. Вибір LiDAR 57 5.3. Raspberry Pi і операційна система 62 5.4. Операційна система робота 65 6. ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ 69 6.1. Загальна характеристика приміщення і робочого місця 70 6.2. Аналіз потенційно небезпечних і шкідливих виробничих факторів на робочому місці 73 6.3. Безпека в надзвичайних ситуаціях 75 ВИСНОВКИ 78 БІБЛІОГРАФІЯ 79 ДОДАТКИ 84
Gomes, Pedro Miguel de Barros. "LADAR based mapping and obstacle detection system for service robots." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/4589.
Повний текст джерелаWhen travelling in unfamiliar environments, a mobile service robot needs to acquire information about his surroundings in order to detect and avoid obstacles and arrive safely at his destination. This dissertation presents a solution for the problem of mapping and obstacle detection in indoor/outdoor structured3 environments, with particular application on service robots equipped with a LADAR. Since this system was designed for structured environments, offroad terrains are outside the scope of this work. Also, the use of any a priori knowledge about LADAR’s surroundings is discarded, i.e. the developed mapping and obstacle detection system works in unknown environments. In this solution, it is assumed that the robot, which carries the LADAR and the mapping and obstacle detection system, is based on a planar surface which is considered to be the ground plane. The LADAR is positioned in a way suitable for a three dimensional world and an AHRS sensor is used to increase the robustness of the system to variations on robot’s attitude, which, in turn, can cause false positives on obstacle detection. The results from the experimental tests conducted in real environments through the incorporation on a physical robot suggest that the developed solution can be a good option for service robots driving in indoor/outdoor structured environments.
Andreasson, Henrik. "Local visual feature based localisation and mapping by mobile robots." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2444.
Повний текст джерелаSchaefer, Alexander [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Highly accurate lidar-based mapping and localization for mobile robots." Freiburg : Universität, 2020. http://d-nb.info/1207756016/34.
Повний текст джерелаBELO, FELIPE AUGUSTO WEILEMANN. "EXPLORATION AND VISUAL MAPPING ALGORITHMS DEVELOPMENT FOR LOW COST MOBILE ROBOTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9142@1.
Повний текст джерелаAo mesmo tempo em que a autonomia de robôs pessoais e domésticos aumenta, cresce a necessidade de interação dos mesmos com o ambiente. A interação mais básica de um robô com o ambiente é feita pela percepção deste e sua navegação. Para uma série de aplicações não é prático prover modelos geométricos válidos do ambiente a um robô antes de seu uso. O robô necessita, então, criar estes modelos enquanto se movimenta e percebe o meio em que está inserido através de sensores. Ao mesmo tempo é necessário minimizar a complexidade requerida quanto a hardware e sensores utilizados. No presente trabalho, um algoritmo iterativo baseado em entropia é proposto para planejar uma estratégia de exploração visual, permitindo a construção eficaz de um modelo em grafo do ambiente. O algoritmo se baseia na determinação da informação presente em sub-regiões de uma imagem panorâmica 2-D da localização atual do robô obtida com uma câmera fixa sobre o mesmo. Utilizando a métrica de entropia baseada na Teoria da Informação de Shannon, o algoritmo determina nós potenciais para os quais deve se prosseguir a exploração. Através de procedimento de Visual Tracking, em conjunto com a técnica SIFT (Scale Invariant Feature Transform), o algoritmo auxilia a navegação do robô para cada nó novo, onde o processo é repetido. Um procedimento baseado em transformações invariáveis a determinadas variações espaciais (desenvolvidas a partir de Fourier e Mellin) é utilizado para auxiliar o processo de guiar o robô para nós já conhecidos. Também é proposto um método baseado na técnica SIFT. Os processos relativos à obtenção de imagens, avaliação, criação do grafo, e prosseguimento dos passos citados continua até que o robô tenha mapeado o ambiente com nível pré-especificado de detalhes. O conjunto de nós e imagens obtidos são combinados de modo a se criar um modelo em grafo do ambiente. Seguindo os caminhos, nó a nó, um robô pode navegar pelo ambiente já explorado. O método é particularmente adequado para ambientes planos. As componentes do algoritmo proposto foram desenvolvidas e testadas no presente trabalho. Resultados experimentais mostrando a eficácia dos métodos propostos são apresentados.
As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. This work proposes an entropy-based iterative algorithm to plan the robot´s visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2- D panoramic image of the environment from the robot´s current location using a single camera fixed on the mobile robot. Using a metric based on Shannon s information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a Visual Tracking process based on SIFT (Scale Invariant Feature Transform), the algorithm helps navigate the robot to each new node, where the imaging process is repeated. An invariant transform (based on Fourier and Mellin) and tracking process is used to guide the robot back to a previous node. Also, an SIFT based method is proposed to accomplish such task. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. The components of the proposed algorithm were developed and tested. Experimental results show the effectiveness of the proposed methods.
Holz, Dirk [Verfasser]. "Efficient 3D Segmentation, Registration and Mapping for Mobile Robots / Dirk Holz." Bonn : Universitäts- und Landesbibliothek Bonn, 2017. http://d-nb.info/1139048856/34.
Повний текст джерелаTrevor, Alexander J. B. "Semantic mapping for service robots: building and using maps for mobile manipulators in semi-structured environments." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53583.
Повний текст джерелаARESTEGUI, NILTON CESAR ANCHAYHUA. "COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR VISUAL SELF-LOCALIZATION AND MAPPING OF MOBILE ROBOTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31775@1.
Повний текст джерелаCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Esta dissertação introduz um estudo sobre os algoritmos de inteligência computacional para o controle autônomo dos robôs móveis, Nesta pesquisa, são desenvolvidos e implementados sistemas inteligentes de controle de um robô móvel construído no Laboratório de Robótica da PUC-Rio, baseado numa modificação do robô ER1. Os experimentos realizados consistem em duas etapas: a primeira etapa de simulação usando o software Player-Stage de simulação do robô em 2-D onde foram desenvolvidos os algoritmos de navegação usando as técnicas de inteligência computacional; e a segunda etapa a implementação dos algoritmos no robô real. As técnicas implementadas para a navegação do robô móvel estão baseadas em algoritmos de inteligência computacional como são redes neurais, lógica difusa e support vector machine (SVM) e para dar suporte visual ao robô móvel foi implementado uma técnica de visão computacional chamado Scale Invariant Future Transform (SIFT), estes algoritmos em conjunto fazem um sistema embebido para dotar de controle autônomo ao robô móvel. As simulações destes algoritmos conseguiram o objetivo, mas na implementação surgiram diferenças muito claras respeito à simulação pelo tempo que demora em processar o microprocessador.
This theses introduces a study on the computational intelligence algorithms for autonomous control of mobile robots, In this research, intelligent systems are developed and implemented for a robot in the Robotics Laboratory of PUC-Rio, based on a modiÞcation of the robot ER1. The verification consist of two stages: the first stage includes simulation using Player-Stage software for simulation of the robot in 2-D with the developed of artiÞcial intelligence; an the second stage, including the implementation of the algorithms in the real robot. The techniques implemented for the navigation of the mobile robot are based on algorithms of computational intelligence as neural networks, fuzzy logic and support vector machine (SVM); and to give visual support to the mobile robot was implemented the visual algorithm called Scale Invariant Future Transform (SIFT), these algorithms in set makes an absorbed system to endow with independent control the mobile robot. The simulations of these algorithms had obtained the objective but in the implementation clear differences had appeared respect to the simulation, it just for the time that delays in processing the microprocessor.
Maffei, Renan de Queiroz. "Translating sensor measurements into texts for localization and mapping with mobile robots." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/158403.
Повний текст джерелаSimultaneous Localization and Mapping (SLAM), fundamental for building robots with true autonomy, is one of the most difficult problems in Robotics and consists of estimating the position of a robot that is moving in an unknown environment while incrementally building the map of such environment. Arguably the most crucial requirement to obtain proper localization and mapping is precise place recognition, that is, determining if the robot is at the same place in different occasions just by looking at the observations taken by the robot. Most approaches in literature are good when using highly expressive sensors such as cameras or when the robot is situated in low ambiguous environments. However this is not the case, for instance, using robots equipped only with range-finder sensors in highly ambiguous indoor structured environments. A good SLAM strategy must be able to handle these scenarios, deal with noise and observation errors, and, especially, model the environment and estimate the robot state in an efficient way. Our proposal in this work is to translate sequences of raw laser measurements into an efficient and compact text representation and deal with the place recognition problem using linguistic processing techniques. First, we translate raw sensor measurements into simple observation values computed through a novel observation model based on kernel-density estimation called Free-Space Density (FSD). These values are quantized into significant classes allowing the division of the environment into contiguous regions of homogeneous spatial density, such as corridors and corners. Regions are represented in a compact form by simple words composed of three syllables – the value of spatial density, the size and the variation of orientation of that region. At the end, the chains of words associated to all observations made by the robot compose a text, in which we search for matches of n-grams (i.e. sequences of words), which is a popular technique from shallow linguistic processing. The technique is also successfully applied in some scenarios of long-term operation, where we must deal with semi-static objects (i.e. that can move occasionally, such as doors and furniture). All approaches were evaluated in simulated and real scenarios obtaining good results.