Optical flow is used in a number of computer vision applications. However, its use in mobile robotic applications is limited because of the high computational complexity involved and the limited availability of computational resources on such platforms. The lack of a hardware that is capable of computing optical flow vector field in real time is a factor that prevents the mobile robotics community to efficiently utilize some successful techniques presented in computer vision literature. In this thesis work, we design and implement a high performance FPGA hardware with a small footprint and low power consumption that is capable of providing over-realtime optical flow data and is hence suitable for this application domain. A well known differential optical flow algorithm presented by Horn &amp / Schunck is selected for this implementation. The complete hardware design of the proposed system is described in details. We also discuss the design alternatives and the selected approaches together with a discussion of the selection procedure. We present the performance analysis of the proposed hardware in terms of computation speed, power consumption and accuracy. The designed hardware is tested with some of the available test sequences that are frequently used for performance evaluations of the optical flow techniques in literature. The proposed hardware is capable of computing optical flow vector field on 256x256 pixels images in 3.89ms which corresponds to a processing speed of 257 fps. The results obtained from FPGA implementation are compared with a floating-point implementation of the same algorithm realized on a PC hardware. The obtained results show that the hardware implementation achieved a superior performance in terms of speed, power consumption and compactness while there is minimal loss of accuracy due to the fixed point implementation.

Aquesta tesi tracta el problema del posicionament de robots mòbils quan, en el decurs del moviment, es realitzen mesures angulars relatives al robot de l'orientació de la recta entre un dels seus punts i punts de l'entorn de posició coneguda. Es considera que les mesures angulars són fetes per un sensor làser giratori que detecta diferents reflectors catadiòptrics fixos.La contribució principal és el desenvolupament d'un algorisme dinàmic, basat en un filtre de Kalman estès (EKF), que estima a cada instant de temps l'estat format pels angles associats als reflectors. La simulació hodomètrica dels angles entre mesures directes del sensor làser garanteix l'ús consistent i continuat dels mètodes de triangulació per a determinar la posició i l'orientació del robot.Inclou simulacions informàtiques i experiments per a validar la precisió del mètode de posicionament proposat. En l'experimentació s'utilitza un robot mòbil omnidireccional amb tres rodes de lliscament direccional de corrons esfèrics. / This thesis focuses on mobile robot positioning methods based on angular measurements, relative to the robot frame and made during its motion, of the orientation of the straight lines between one of its points and known artificial landmarks. The angular measurements are assumed to be done by a rotating laser scanner that detects different catadioptric landmarks on the workspace.Its main contribution is the development of a dynamic algorithm based on an extended Kalman filter (EKF) that estimates at any time the state-vector of the landmark relative angles. The odometric simulation of landmark angles between actual measurements guarantees the consistent and continuous use of the triangulation methods to determine the robot position and orientation.It includes computer simulations and experiments in order to validate the accuracy of the proposed positioning method. In the experiments, an omnidirectional mobile robot with three directionally sliding wheels made of spherical rollers has been used.

Aujourd’hui, la localisation de robots sous-marins demeure une tâche complexe. L’utilisation de capteurs habituels est impossible sous la surface, tels que ceux reposant sur les systèmes de géolocalisation par satellites. Les approches inertielles sont quant à elles limitées par leur forte dérive dans le temps. De plus, les fonds marins sont généralement homogènes et non structurés, rendant difficile l’utilisation de méthodes SLAM connues, qui couplent la localisation et la cartographie de manière simultanée. Il devient donc nécessaire d’explorer de nouvelles alternatives. Notre approche consiste à traiter un problème de SLAM de manière purement temporelle. L’originalité de ce travail est de représenter le temps comme une variable classique qu’il faut estimer. Cette stratégie soulève de nouvelles opportunités dans le domaine de l’estimation d’état, permettant de traiter de nombreux problèmes sous un autre angle. Toutefois, une telle résolution temporelle demande un ensemble d’outils théoriques qu’il convient de développer. Cette thèse n’est donc pas seulement une contribution dans le monde de la robotique mobile, elle propose également une nouvelle démarche dans les domaines de la propagation de contraintes et des méthodes ensemblistes. Cette étude apporte de nouveaux outils de programmation par contracteurs qui permettent le développement de solveurs pour des systèmes dynamiques. Les composants étudiés sont mis en application tout au long de ce document autours de problèmes robotiques concrets. / The localization of underwater robots remains a challenging issue. Usual sensors, such as Global NavigationSatellite System (GNSS) receivers, cannot be used under the surface and other inertial systems suffer from a strong integration drift. On top of that, the seabed is generally uniform and unstructured, making it difficult to apply usual Simultaneous Localization and Mapping (SLAM) methods to perform a localization.Hence, innovative approaches have to be explored. The presented method can be characterized as a raw-data SLAM approach, but we propose a temporal resolution – which differs from usual methods – by considering time as a standard variable to be estimated. This concept raises new opportunities for state estimation, under-exploited so far. However, such temporal resolution is not straightforward and requires a set of theoretical tools in order to achieve the main purpose of localization.This thesis is thus not only a contribution in the field of mobile robotics, it also offers new perspectives in the areas of constraint programming and set-membership approaches. We provide a reliable contractor programming framework in order to build solvers for dynamical systems. This set of tools is illustrated throughout this document with realistic robotic applications.

As we navigate the world, for example when driving a car from our home to the work place, we continuously perceive the 3D structure of our surroundings and intuitively recognise the objects we see. Such capabilities help us in our everyday lives and enable free and accurate movement even in completely unfamiliar places. We largely take these abilities for granted, but for robots, the task of understanding large outdoor scenes remains extremely challenging. In this thesis, I develop novel algorithms for (near) real-time dense 3D reconstruction and semantic segmentation of large-scale outdoor scenes from passive cameras. Motivated by "smart glasses" for partially sighted users, I show how such modeling can be integrated into an interactive augmented reality system which puts the user in the loop and allows her to physically interact with the world to learn personalized semantically segmented dense 3D models. In the next part, I show how sparse but very accurate 3D measurements can be incorporated directly into the dense depth estimation process and propose a probabilistic model for incremental dense scene reconstruction. To relax the assumption of a stereo camera, I address dense 3D reconstruction in its monocular form and show how the local model can be improved by joint optimization over depth and pose. The world around us is not stationary. However, reconstructing dynamically moving and potentially non-rigidly deforming texture-less objects typically require "contour correspondences" for shape-from-silhouettes. Hence, I propose a video segmentation model which encodes a single object instance as a closed curve, maintains correspondences across time and provide very accurate segmentation close to object boundaries. Finally, instead of evaluating the performance in an isolated setup (IoU scores) which does not measure the impact on decision-making, I show how semantic 3D reconstruction can be incorporated into standard Deep Q-learning to improve decision-making of agents navigating complex 3D environments.

L'estimation précise du mouvement 3D d'une caméra relativement à une scène rigideest essentielle pour tous les systèmes de navigation visuels. Aujourd'hui différentstypes de capteurs sont adoptés pour se localiser et naviguer dans des environnementsinconnus : GPS, capteurs de distance, caméras, capteurs magnétiques, centralesinertielles (IMU, Inertial Measurement Unit). Afin d'avoir une estimation robuste, lesmesures de plusieurs capteurs sont fusionnées. Même si le progrès technologiquepermet d'avoir des capteurs de plus en plus précis, et si la communauté de robotiquemobile développe algorithmes de navigation de plus en plus performantes, il y aencore des défis ouverts. De plus, l'intérêt croissant des la communauté de robotiquepour les micro robots et essaim de robots pousse vers l'emploi de capteurs à bas poids,bas coût et vers l'étude d'algorithmes à faible complexité. Dans ce contexte, capteursinertiels et caméras monoculaires, grâce à leurs caractéristiques complémentaires,faible poids, bas coût et utilisation généralisée, représentent une combinaison decapteur intéressante.Cette thèse présente une contribution dans le cadre de la navigation inertielle assistéepar vision et aborde les problèmes de fusion de données et estimation de la pose, envisant des algorithmes à faible complexité appliqués à des micro-véhicules aériens.Pour ce qui concerne l'association de données, une nouvelle méthode pour estimer lemouvement relatif entre deux vues de caméra consécutifs est proposée.Celle-ci ne nécessite l'observation que d'un seul point caractéristique de la scène et laconnaissance des vitesses angulaires fournies par la centrale inertielle, sousl'hypothèse que le mouvement de la caméra appartient localement à un planperpendiculaire à la direction de la gravité. Deux algorithmes très efficaces pouréliminer les fausses associations de données (outliers) sont proposés sur la base decette hypothèse de mouvement.Afin de généraliser l'approche pour des mouvements à six degrés de liberté, deuxpoints caracteristiques et les données gyroscopiques correspondantes sont nécessaires.Dans ce cas, deux algorithmes sont proposés pour éliminer les outliers.Nous montrons que dans le cas d'une caméra monoculaire montée sur un quadrotor,les informations de mouvement fournies par l'IMU peuvent être utilisées pouréliminer de mauvaises estimations.Pour ce qui concerne le problème d'estimation de la pose, cette thèse fournit unesolution analytique pour exprimer la pose du système à partir de l'observation de troispoints caractéristiques naturels dans une seule image, une fois que le roulis et letangage sont obtenus par les données inertielles sous l'hypothèse de terrain plan.Afin d'aborder le problème d'estimation de la pose dans des environnements sombresou manquant de points caractéristiques, un système équipé d'une caméra monoculaire,d'une centrale inertielle et d'un pointeur laser est considéré. Grace à une analysed'observabilité il est démontré que les grandeurs physiques qui peuvent êtredéterminées par l'exploitation des mesures fourni par ce systeme de capteurs pendantun court intervalle de temps sont : la distance entre le système et la surface plane ;la composante de la vitesse du système qui est orthogonale à la surface ; l'orientationrelative du système par rapport à la surface et l'orientation de la surface par rapport àla gravité. Une méthode récursive simple a été proposée pour l'estimation de toutesces quantités observables.Toutes les contributions de cette thèse sont validées par des expérimentations à l'aidedes données réelles et simulées. Grace à leur faible complexité de calcul, lesalgorithmes proposés sont très appropriés pour la mise en oeuvre en temps réel surdes systèmes ayant des ressources de calcul limitées. La suite de capteur considéréeest monté sur un quadrotor, mais les contributions de cette thèse peuvent êtreappliquées à n'importe quel appareil mobile. / Accurate egomotion estimation is of utmost importance for any navigation system.Nowadays di_erent sensors are adopted to localize and navigate in unknownenvironments such as GPS, range sensors, cameras, magnetic field sensors, inertialsensors (IMU). In order to have a robust egomotion estimation, the information ofmultiple sensors is fused. Although the improvements of technology in providingmore accurate sensors, and the efforts of the mobile robotics community in thedevelopment of more performant navigation algorithms, there are still openchallenges. Furthermore, the growing interest of the robotics community in microrobots and swarm of robots pushes towards the employment of low weight, low costsensors and low computational complexity algorithms. In this context inertial sensorsand monocular cameras, thanks to their complementary characteristics, low weight,low cost and widespread use, represent an interesting sensor suite.This dissertation represents a contribution in the framework of vision-aided inertialnavigation and tackles the problems of data association and pose estimation aimingfor low computational complexity algorithms applied to MAVs.For what concerns the data association, a novel method to estimate the relative motionbetween two consecutive camera views is proposed. It only requires the observationof a single feature in the scene and the knowledge of the angular rates from an IMU,under the assumption that the local camera motion lies in a plane perpendicular to thegravity vector. Two very efficient algorithms to remove the outliers of the featurematchingprocess are provided under the abovementioned motion assumption. Inorder to generalize the approach to a 6DoF motion, two feature correspondences andgyroscopic data from IMU measurements are necessary. In this case, two algorithmsare provided to remove wrong data associations in the feature-matching process. Inthe case of a monocular camera mounted on a quadrotor vehicle, motion priors fromIMU are used to discard wrong estimations.For what concerns the pose estimation problem, this thesis provides a closed formsolution which gives the system pose from three natural features observed in a singlecamera image, once the roll and the pitch angles are obtained by the inertialmeasurements under the planar ground assumption.In order to tackle the pose estimation problem in dark or featureless environments, asystem equipped with a monocular camera, inertial sensors and a laser pointer isconsidered. The system moves in the surrounding of a planar surface and the laserpointer produces a laser spot on the abovementioned surface. The laser spot isobserved by the monocular camera and represents the only point feature considered.Through an observability analysis it is demonstrated that the physical quantities whichcan be determined by exploiting the measurements provided by the aforementionedsensor suite during a short time interval are: the distance of the system from the planarsurface; the component of the system speed that is orthogonal to the planar surface;the relative orientation of the system with respect to the planar surface; the orientationof the planar surface with respect to the gravity. A simple recursive method toperform the estimation of all the aforementioned observable quantities is provided.All the contributions of this thesis are validated through experimental results usingboth simulated and real data. Thanks to their low computational complexity, theproposed algorithms are very suitable for real time implementation on systems withlimited on-board computation resources. The considered sensor suite is mounted on aquadrotor vehicle but the contributions of this dissertations can be applied to anymobile device.

CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / Este trabalho investiga o grau de influÃncia que a inclusÃo de mecanismos de memÃria de curta duraÃÃo (MCD) exercem sobre o desempenho de classificadores neurais quando aplicados em tarefas de navegaÃÃo de robÃs. Em particular, trata da navegaÃÃo do tipo Wall Following. Para este fim, quatro conhecidas arquiteturas neurais (Perceptron LogÃstico, Perceptron Multicamadas, Mistura de Especialistas e rede de Elman) sÃo usadas com o intuito de associar diferentes padrÃes de leituras sensoriais com quatro classes de aÃÃes prÃ-determinadas. Todas as etapas dos experimentos - aquisiÃÃo dos dados, seleÃÃo e treinamento das arquiteturas em simulador, alÃm da execuÃÃo das mesmas em robà mÃvel real (SCITOS G5) - sÃo escritas em detalhes. Os resultados obtidos sugerem que a tarefa de seguir paredes, formulada como um problema de classificaÃÃo de padrÃes, à nÃo-linearmente separÃvel, resultado este que favorece a rede MLP quando os classificadores sÃo treinados sem MCD. Contudo, se mecanismos de MCD sÃo usados, entÃo atà mesmo uma rede linear à capaz de executar a tarefa de interesse com sucesso / This work reports results of an investigation on the degree of influence that the inclusion of short-term memory mechanisms has on the performance of neural classifiers when applied to robot navigation tasks. In particular, we deal with the well-known strategy of navigating by âwall-followingâ. For this purpose, four neural architectures (Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman network) are used to associate different sensory input patterns with four predetermined action categories. All stages of the experiments - data acquisition, selection and training of the architectures in a simulator and their execution on a real mobile robot - are described. The obtained results suggest that the wall-following task, formulated as a pattern classification problem, is nonlinearly separable, a result that favors the MLP network if no memory of input patterns are taken into account. If short-term memory mechanisms are used, then even a linear network is able to perform the same task successfully

Este trabalho apresenta uma arquitetura de hardware, baseada em FPGA (Field-Programmable Gate Array) e com multi-câmeras, para o problema de localização e mapeamento simultâneos - SLAM (Simultaneous Localization And Mapping) aplicada a sistemas robóticos embarcados. A arquitetura é composta por módulos de hardware altamente especializados para a localização do robô e para geração do mapa do ambiente de navegação em tempo real com features extraídas de imagens obtidas diretamente de câmeras CMOS a uma velocidade de 30 frames por segundo. O sistema é totalmente embarcado em FPGA e apresenta desempenho superior em, pelo menos, uma ordem de magnitude em relaçãoo às implementações em software processadas por computadores pessoais de última geração. Esse desempenho deve-se à exploração do paralelismo em hardware junto com o processamento em pipeline e às otimizações realizadas nos algoritmos. As principais contribuições deste trabalho são as arquiteturas para o filtro de Kalman estendido - EKF (Extended Kalman Filter) e para a detecção de features baseada no algoritmo SIFT (Scale Invariant Feature Transform). A complexidade para a implementaçãoo deste trabalho pode ser considerada alta, uma vez que envolve uma grande quantidade de operações aritméticas e trigonométricas em ponto utuante e ponto fixo, um intenso processamento de imagens para extração de features e verificação de sua estabilidade e o desenvolvimento de um sistema de aquisição de imagens para quatro câmeras CMOS em tempo real. Adicionalmente, foram criadas interfaces de comunicação para o software e o hardware embarcados no FPGA e para o controle e leitura dos sensores do robô móvel. Além dos detalhes e resultados da implementação, neste trabalho são apresentados os conceitos básicos de mapeamento e o estado da arte dos algoritmos SLAM com visão monocular e estéreo / This work presents a hardware architecture for the Simultaneous Localization And Mapping (SLAM) problem applied to embedded robots. This architecture, which is based on FPGA and multi-cameras, is composed by highly specialized blocks for robot localization and feature-based map building in real time from images read directly from CMOS cameras at 30 frames per second. The system is completely embedded on an FPGA and its performance is at least one order of magnitude better than a high end PC-based implementation. This result is achieved by investigating the impact of several hardwareorientated optimizations on performance and by exploiting hardware parallelism along with pipeline processing. The main contributions of this work are the architectures for the Extended Kalman Filter (EKF) and for the feature detection system based on the SIFT (Scale Invariant Feature Transform). The complexity to implement this work can be considered high, as it involves a significant number of arithmetic and trigonometric operations in oating and fixed-point format, an intensive image processing for feature detection and stability checking, and the development of an image acquisition system from four CMOS cameras in real time. In addition, communication interfaces were created to integrate software and hardware embedded on FPGA and to control the mobile robot base and to read its sensors. Finally, besides the implementation details and the results, this work also presents basic concepts about mapping and state-of-the-art algorithms for SLAM with monocular and stereo vision.

O adequado deslocamento autônomo de robôs móveis entre fileiras de cultura agrícola implica a apropriada configuração estrutural do veículo, bem como considerar a detecção das filas de plantas ou árvores, e também o desenvolvimento de um sistema de controle de locomoção. Esta tese apresenta o desenvolvimento de um sistema de controle em malha fechada baseado na técnica de otimização H∞, que é aplicado no deslocamento entre fileiras de plantio de um robô móvel agrícola em escala reduzida. Mais especificamente, o foco deste trabalho é o seguimento de caminhos na cultura através da aplicação de técnicas de controle robusto. Duas questões foram fundamentais na elaboração da tese: 1) \"Quais são os métodos e procedimentos necessários para implementar a navegação autônoma de um protótipo de robô móvel entre fileiras de cultura agrícola?\" e, 2) \"É possível aplicar os conceitos relativos a sistemas de controle em malha fechada para solucionar o problema da navegação autônoma do robôs móveis entre fileiras de cultura agrícola?\". Primeiramente é apresentada uma revisão bibliográfica sobre robôs móveis agrícolas que tem locomoção baseada em rodas. Posteriormente, os conceitos relacionados com o projeto de controle baseado na técnica de otimização H∞ são fundamentados. Em seguida, são descritos os detalhes relacionados com a construção da plataforma robótica proposta, o projeto do controlador de caminho, as respectivas simulações e as especificações para a realização de testes em ambiente agrícola. Finalmente os resultados alcançados são apresentados. Conclui-se que o sistema de controle proposto se mostrou efetivo na realização da navegação autônoma do robô entre as fileiras da cultura previamente configuradas para a avaliação do seu desempenho. / Appropriate autonomous navigation of mobile robots between crop rows implies, besides appropriate structural configuration, considering detection of plants or trees in rows, as well as the development of a locomotion control system. Consequently, this thesis presents the development of a closed loop control system based on H∞ optimization technique, which is applied to control the navigation of a low scale car-like mobile robot between crop rows; more specifically, main focus of this work is tracking paths in the culture, by the application of robust control techniques. Two questions were fundamental in the development of the thesis: 1) which are the methods and procedures to implement the autonomous navigation of a mobile robot prototype between crop rows? And 2) is it possible to apply the concepts of closed-loop control systems to solve the problem of autonomous navigation of mobile robots between crop rows? Firstly, we provide a literature review on agricultural mobile robots whose mobility depends on wheels. Secondly, control systems design fundamentals based on the H∞ optimization technique are addressed. Thirdly, details related to the construction of the proposed robotic platform and also the design of the proposed path controller (including its simulation and specifications for testing within an agricultural environment) are described. Finally, results of our findings are presented. It is concluded that our control system showed to be effective in the realization of autonomous navigation between crop rows in agricultural environment, which was properly configured in order to evaluate the performance of our robot.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2017-03-24T21:09:18Z No. of bitstreams: 2 Dissertação - Lucas da Silva Assis - 2017.pdf: 4403122 bytes, checksum: b6716ca532c65ba98f07fab680e6569d (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2017-03-28T11:39:32Z (GMT) No. of bitstreams: 2 Dissertação - Lucas da Silva Assis - 2017.pdf: 4403122 bytes, checksum: b6716ca532c65ba98f07fab680e6569d (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-03-28T11:39:32Z (GMT). No. of bitstreams: 2 Dissertação - Lucas da Silva Assis - 2017.pdf: 4403122 bytes, checksum: b6716ca532c65ba98f07fab680e6569d (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-02-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / between desired points. These optimal paths can be understood as trajectories that best achieves an objective, e.g. minimizing the distance travelled or the time spent. Most of usual path planning techniques assumes a complete and accurate environment model to generate optimal paths. But many of the real world problems are in the scope of Local Path Planning, i.e. working with partially known or unknown environments. Therefore, these applications are usually restricted to sub-optimal approaches which plan an initial path based on known information and then modifying the path locally or re-planning the entire path as the agent discovers new obstacles or environment features. Even though traditional path planning strategies have been widely used in partially known environments, their sub-optimal solutions becomes even worse when the size or resolution of the environment's representation scale up. Thus, in this work we present the CGPlan (Constructive Genetic Planning), a new evolutionary approach based on the Compact Genetic Algorithm (cGA) that pursue efficient path planning in known and unknown environments. The CGPlan was evaluated in simulated environments with increasing complexity and compared with common techniques used for path planning, such as the A*, the BUG2 algorithm, the RRT (Rapidly-Exploring Random Tree) and the evolutionary path planning based on classic Genetic Algorithm. The results shown a great efficient of the proposal and thus indicate a new reliable approach for path planning of mobile agents with limited computational power and real-time constraints on on-board hardware. / O planejamento de rotas é um recurso importante para agentes móveis, permitindo-lhes encontrar caminhos ideais entre os pontos desejados. Neste contexto, caminhos ideais podem ser entendidos como trajetórias que melhor atingem um objetivo, minimizando a distância percorrida ou o tempo gasto, por exemplo. As técnicas tradicionais tendem a considerar um modelo global do ambiente, no entanto, os problemas reais de planejamento de rotas usualmente estão no âmbito de ambientes desconhecidos ou parcialmente desconhecidos. Portanto, aplicações como essas geralmente são restritas a abordagens subótimas que planejam um caminho inicial baseado em informações conhecidas e, em seguida, modificam o caminho localmente ou até planejando novamente todo o caminho à medida que o agente descobre novos obstáculos ou características do ambiente. Sendo assim, mesmo as estratégias tradicionais de planejamento de caminhos sendo amplamente utilizadas em ambientes parcialmente conhecidos, suas soluções subótimas se tornam ainda piores quando o tamanho ou a resolução da representação do ambiente aumentam. Por isso, neste trabalho apresentamos o CGPlan (Constructive Genetic Planning), uma nova abordagem evolutiva baseada no Algoritmo Genético Compacto (cGA) que almeja um planejamento eficiente de caminho em ambientes conhecidos e desconhecidos. O CGPlan foi avaliado em ambientes simulados com crescente complexidade e comparado a técnicas comuns utilizadas para o planejamento do caminho, como o A*, o algoritmo BUG2, o RRT (Rapidly-Exploring Random Tree) e o planejamento evolutivo do caminho usando clássico Algoritmo Genético. Os resultados mostraram uma grande eficiência da proposta e indicam uma nova abordagem confiável para o planejamento de rotas de agentes móveis com poder computacional limitado e restrições em tempo real no hardware.

Cette thèse s’inscrit dans le cadre d’un projet visant à développer un robot mobile autonome capable de réaliser des tâches spécifiques dans une zone préalablement définie par l’utilisateur. Afin de faciliter la mise en œuvre du système, des balises radiofréquences fournissant des mesures de distance par rapport au robot sont disposées au préalable autour du terrain. Le déploiement du robot s’effectue en deux phases, une première d’apprentissage du terrain, puis une seconde, où le robot effectue ses tâches de façon autonome. Ces deux étapes nécessitent de résoudre les problèmes de localisation et de localisation et cartographie simultanées pour lesquels différentes solutions sont proposées et testées en simulation et sur des jeux de données réelles. De plus, afin de faciliter l’installation et d’améliorer les performances du système, un algorithme de placement des balises est présenté puis testé en simulation afin de valider notamment l’amélioration des performances de localisation. / This thesis is part of a project which aims at developing an autonomous mobile robot able to perform specific tasks in a preset area. To ease the setup of the system, radio-frequency beacons providing range measurements with respect to the robot are set up beforehand on the borders of the robot’s workspace. The system deployment consists in two steps, one for learning the environment, then a second, where the robot executes its tasks autonomously. These two steps require to solve the localization and simultaneous localization and mapping problems for which several solutions are proposed and tested in simulation and on real datasets. Moreover, to ease the setup and improve the system performances, a beacon placement algorithm is presented and tested in simulation in order to validate in particular the improvement of the localization performances.