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dc.contributorSanfeliu Cortés, Alberto
dc.contributorAndrade-Cetto, Juan
dc.contributor.authorVidal Calleja, Teresa Alejandra
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2011-04-12T15:09:47Z
dc.date.available2007-09-10
dc.date.issued2007-07-13
dc.date.submitted2007-07-19
dc.identifier.citationVidal Calleja, T.A. Visual Navigation in Unknown Environments. Tesi doctoral, UPC, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, 2007. ISBN 9788469083314. DOI 10.5821/dissertation-2117-93509.
dc.identifier.isbn9788469083314
dc.identifier.otherhttp://www.tdx.cat/TDX-0719107-100000
dc.identifier.urihttp://hdl.handle.net/2117/93509
dc.description.abstractNavigation in mobile robotics involves two tasks, keeping track of the robot's position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings as it moves. These three problems ought to be solved in conjunction since they depend on each other. <br/>This thesis is about simultaneously controlling an autonomous vehicle, estimating its location and building the map of the environment. The main objective is to analyse the problem from a control theoretical perspective based on the EKF-SLAM implementation. <br/>The contribution of this thesis is the analysis of system's properties such as observability, controllability and stability, which allow us to propose an appropriate navigation scheme that produces well-behaved estimators, controllers, and consequently, the system as a whole. <br/>We present a steady state analysis of the SLAM problem, identifying the conditions that lead to partial observability. It is shown that the effects of partial observability appear even in the ideal linear Gaussian case. This indicates that linearisation alone is not the only cause of SLAM inconsistency, and that observability must be achieved as a prerequisite to tackling the effects of linearisation. Additionally, full observability is also shown to be necessary during diagonalisation of the covariance matrix, an approach often used to reduce the computational complexity of the SLAM algorithm, and which leads to full controllability as we show in this work.<br/>Focusing specifically on the case of a system with a single monocular camera, we present an observability analysis using the nullspace basis of the stripped observability matrix. The aim is to get a better understanding of the well known intuitive behaviour of this type of systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates between vehicle and camera. Through characterisation the unobservable directions in monocular SLAM, we are able to identify the vehicle motions required to maximise the number of observable states in the system. <br/>When closing the control loop of the SLAM system, both the feedback controller and the estimator are shown to be asymptotically stable. Furthermore, we show that the tracking error does not influence the estimation performance of a fully observable system and viceversa, that control is not affected by the estimation. Because of this, a higher level motion strategy is required in order to enhance estimation, specially needed while performing SLAM with a single camera. Considering a real-time application, we propose a control strategy to optimise both the localisation of the vehicle and the feature map by computing the most appropriate control actions or movements. The actions are chosen in order to maximise an information theoretic metric. Simulations and real-time experiments are performed to demonstrate the feasibility of the proposed control strategy.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rightsADVERTIMENT. L'accés als continguts d'aquesta tesi doctoral i la seva utilització ha de respectar els drets de la persona autora. Pot ser utilitzada per a consulta o estudi personal, així com en activitats o materials d'investigació i docència en els termes establerts a l'art. 32 del Text Refós de la Llei de Propietat Intel·lectual (RDL 1/1996). Per altres utilitzacions es requereix l'autorització prèvia i expressa de la persona autora. En qualsevol cas, en la utilització dels seus continguts caldrà indicar de forma clara el nom i cognoms de la persona autora i el títol de la tesi doctoral. No s'autoritza la seva reproducció o altres formes d'explotació efectuades amb finalitats de lucre ni la seva comunicació pública des d'un lloc aliè al servei TDX. Tampoc s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant als continguts de la tesi com als seus resums i índexs.
dc.sourceTDX (Tesis Doctorals en Xarxa)
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.othermapping
dc.subject.otherlocalization
dc.subject.othermobile robotics
dc.subject.othernavigation
dc.subject.othercontrol
dc.subject.othervision
dc.subject.otherSLAM
dc.subject.otherunknown environments
dc.titleVisual Navigation in Unknown Environments
dc.typeDoctoral thesis
dc.subject.lemacRobòtica
dc.subject.lemacRobots mòbils
dc.subject.lemacVisió per ordinador
dc.identifier.doi10.5821/dissertation-2117-93509
dc.identifier.dlB.48081-2007
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.identifier.tdxhttp://hdl.handle.net/10803/6197


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