Um componente fundamental no sistema de um robô móvel consiste na habilidade de localizar-se acuradamente, o que envolve estimar sua postura em relação a uma representação global do espaço. A especificação geral de uma abordagem de localização baseada em dados sensoriais possui uma estimativa inicial da postura do robô e usa os dados coletados pelos sensores, em conjunto com um mapa do ambiente, para produzir uma estimativa mais precisa da postura, que oferece um valor de maior confiança em relação à postura real do robô. Uma dificuldade é que os dados sensoriais são corrompidos por erros de medidas derivados de diversas fontes, como ruídos, quantização, dispositivos de digitalização, deslizamentos do robô, entre outras. Sensores distintos medem diferentes propriedades físicas, corrompidas por diversos erros de medida. O uso de dados oriundos de vários sensores fornece informação redundante e complementar, que pode ser processada para derivar uma estimativa combinada com o objetivo de aumentar a confiança na estimativa final da postura. Nesta dissertação é proposto ELViS, um sistema que estima a localização de um robô móvel equipado com odômetros, uma câmera de vídeo e um semi-anel frontal de 8 sonares, o qual opera, com sucesso, em um ambiente interno, estruturado e estático. Assume-se que o robô navega sobre uma superfície plana e que diversos segmentos de retas possam ser identificados nas imagens do ambiente. Para aumentar a seletividade dos marcos visuais e diminuir a complexidade computacional no processamento e correspondência dos dados com os modelos, elementos do ambiente são representados por modelos minimalistas, possibilitando o uso do ELViS em um grande número de aplicações onde o custo ou tempo de execução sejam fatores limitantes. ELViS foi implementado e testado utilizando dois estimadores baseados em Filtro de Kalman. Os resultados, obtidos com robôs reais e em simulações, indicam direções bastante promissoras. / A key component of a mobile robot system is the ability to localize itself accurately, which involves estimating its pose with respect to some global representation of space. The general specification of a sensor-based localization approach starts with an initial estimate of the robot\'s pose and uses sensor data in conjunction with a map to produce a refined pose estimate that has an increased confidence about the true pose of the robot. One of the main difficulties is that sensor data is corrupted by measurement errors. These errors can arise from noise, quantization, digitalization artifacts, wheel slippage, and other such sources. Different sensors measure different physical properties, which are corrupted by different sources of measurement errors. The use of data from multiple sensors provides redundant and complementary information that can be processed to obtain a combined estimate aiming at an increase in the confidence of the final pose estimate. In this work we propose ELViS, a system that estimates the localization of a mobile robot equipped with odometers, a video camera and a frontal semi-ring of 8 sonar sensors, and that operates successfully in stationary and structured indoor environments. It is assumed that the robot navigates on flat surfaces and that straight lines can be identified in the environment image acquired by the camera. To increase selectivity of the landmarks and reduce computational complexity in data processing and matching to the map, environment features are represented using minimalist models in the map. This allows the use of ELViS in a large number of applications where tight budget or execution time constraints exist. ELViS has been implemented and tested using two estimators based on the Kalman Filter. The results, obtained with the real robots and in series of simulation runs, indicate promising directions.

O filtro de Kalman estendido tem sido a mais popular ferramenta de filtragem não linear das últimas quatro décadas. É de fácil implementação e apresenta baixo custo computacional. Nos casos nos quais as não linearidades do sistema dinâmico são significativas, porém, o filtro de Kalman estendido pode apresentar resultados insatisfatórios. Nessas situações, o filtro de Kalman unscented substitui com vantagens o filtro de Kalman estendido, pois pode apresentar melhores estimativas de estado, embora ambos os filtros exibam complexidade computacional de mesma ordem. A qualidade das estimativas de estado do filtro unscented está intimamente ligada à sintonia dos parâmetros que controlam a transformada unscented. A versão escalada dessa transformada exibe três parâmetros escalares que determinam o posicionamento dos pontos sigma e, consequentemente, afetam diretamente a qualidade das estimativas produzidas pelo filtro. Apesar da importância do filtro de Kalman unscented, a sintonia ótima desses parâmetros é um problema para o qual ainda não há solução definitiva. Não há nem mesmo recomendações heurísticas que garantam o bom funcionamento do filtro unscented na maior parte dos problemas tratáveis por meio de filtros Gaussianos. Essa carência e a importância desse filtro para a área de filtragem não linear fazem da busca por mecanismos de sintonia automática do filtro unscented área de pesquisa ativa. Assim, este trabalho propõe técnicas para sintonia automática dos parâmetros da transformada unscented escalada. Além da sintonia desses parâmetros, também é abordado o problema de sintonizar as matrizes de covariância dos ruídos de processo e de medida demandadas pelo modelo do sistema dinâmico usado pelo filtro unscented. As técnicas propostas cobrem então a sintonia automática de todos os parâmetros do filtro. / The extended Kalman filter has been the most popular nonlinear filter of the last four decades. It is easy to implement and exhibits low computational cost. When nonlinearities are significant, though, the extended Kalman filter can display poor state estimation performance. In such situations, the unscented Kalman filter can yield better state estimates, while displaying the same order of computational complexity as the extended Kalman filter. The quality of the state estimates produced by the unscented Kalman filter is directly influenced by the tuning of the scalar parameters that govern the unscented transform. The scaled version of the unscented transform features three scalar parameters that determine the positioning of the sigma points, thus directly affecting the filter state estimation performance. Despite the importance of the unscented Kalman filter, the optimal tuning of the scaled unscented transform parameters is still an open problem. This work hence discusses algorithms for the automatic tuning of the unscented transform parameters. The discussion includes the tuning of the needed noise covariance matrices, thus covering the automatic tuning of all parameters of the unscented Kalman filter.

This thesis demonstrates that any individual can provide relevant observational data to further research efforts within the Aerospace community, through the use of amateur telescopes. A Meade LX200 12 in. telescope and Lumenera Skynyx 2.0 camera were utilized to observe small LEO satellites, using a well-documented point-and-wait staring method. Over a period of three months, a total of 186 observation attempts were made resulting in 97 successful captures. From the gathered data, three possible aerospace applications were analyzed: validation of a satellite brightness prediction model, angles-only orbit determination including extended Kalman filtering, and temporal error growth in TLE-based orbit propagation. Further investigations include a preliminary optimization using MATLAB's fmincon function (informed by the previous analyses) to determine an optimal telescope size for performing LEO observations.

Zugl.: Saarbrücken, Univ., Diss., 2007

<p>This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.</p>

<p>With an increasing traffic intensity the demands on vehicular safety is higher than ever before. Active safety systems that have been developed recent years are a response to that. In this master thesis Sensor Fusion is used to combine information from a laser scanner and a microwave radar in order to get more information about the surroundings in front of a vehicle. The Extended Kalman Filter method has been used to fuse the information from the sensors. The process model consists partly of a Constant Turn model to describe the motion of the ego vehicle as well as a tracked object. These individual motions are then put together in a framework for spatial relationships to describe the relationship between them. Two measurement models have been used to describe the two sensors. They have been derived from a general sensor model. This filter approach has been used to estimate the position and orientation of an object relative the ego vehicle. Also velocity, yaw rate and the width of the object have been estimated. The filter has been implemented and simulated in Matlab. The data that has been recorded and used in this work is coming from a scenario where the ego vehicle is following an object in a quite straight line. Where the ego vehicle is a truck and the object is a bus. One important conclusion from this work is that the filter is sensitive to the number of laser beams that hits the object of interest. No qualitative validation has been made though.</p>

In this paper we show how the theory of dynamical systems can be employed to solve problems in motion vision. In particular we develop algorithms for the recovery of dense depth maps and motion parameters using state space observers or filters. Four different dynamical models of the imaging situation are investigated and corresponding filters/ observers derived. The most powerful of these algorithms recovers depth and motion of general nature using a brightness change constraint assumption. No feature-matching preprocessor is required.

This thesis investigates the problem of estimating the three-dimensional structure of a scene from a sequence of images. Structure information is recovered from images continuously using shading, motion or other visual mechanisms. A Kalman filter represents structure in a dense depth map. With each new image, the filter first updates the current depth map by a minimum variance estimate that best fits the new image data and the previous estimate. Then the structure estimate is predicted for the next time step by a transformation that accounts for relative camera motion. Experimental evaluation shows the significant improvement in quality and computation time that can be achieved using this technique.

This dissertation presents an experimental and analytical study of the Vision Based Navigation system (VisNav). VisNav is a novel intelligent optical sensor system invented by Texas A&M University recently for autonomous proximity operations. This dissertation is focused on system calibration techniques and navigation algorithms. This dissertation is composed of four parts. First, the fundamental hardware and software design configuration of the VisNav system is introduced. Second, system calibration techniques are discussed that should enable an accurate VisNav system application, as well as characterization of errors. Third, a new six degree-of-freedom navigation algorithm based on the Gaussian Least Squares Differential Correction is presented that provides a geometrical best position and attitude estimates through batch iterations. Finally, a dynamic state estimation algorithm utilizing the Extended Kalman Filter (EKF) is developed that recursively estimates position, attitude, linear velocities, and angular rates. Moreover, an approach for integration of VisNav measurements with those made by an Inertial Measuring Unit (IMU) is derived. This novel VisNav/IMU integration technique is shown to significantly improve the navigation accuracy and guarantee the robustness of the navigation system in the event of occasional dropout of VisNav data.

Résumé : Le travail réalisé au cours de cette thèse traite de la mise au point de méthodes d’estimation d’état robuste, avec deux domaines d’application en ligne de mire. Le premier concerne le positionnement sécuritaire en transport. L’objectif est de fournir la position et la vitesse du véhicule sous la forme d’intervalles avec un grand degré de confiance. Le second concerne la synthèse de capteurs logiciels pour les bioprocédés, et en particulier la reconstruction des concentrations de composants réactionnels à partir d’un nombre limité de mesures et d’un modèle mathématique interprétant le comportement dynamique de ces composants. L’objectif principal est de concevoir des algorithmes qui puissent fournir des estimations acceptables en dépit des incertitudes provenant de la mauvaise connaissance du système comme les incertitudes sur les paramètres du modèle ou les incertitudes de mesures. Dans ce contexte, plusieurs algorithmes ont été étudiés et mis au point. Ainsi, dans le cadre du positionnement de véhicule, la recherche s’est dirigée vers les méthodes robustes Hinfini et les méthodes par intervalles. Les méthodes Hinfini sont des méthodes linéaires prenant en compte une incertitude dans la modélisation et réalisant une optimisation min-max, c’est-à-dire minimisant une fonction de coût qui représente la pire situation compte tenu des incertitudes paramétriques. La contribution de ce travail concerne l’extension à des modèles faiblement non linéaires et l’utilisation d’une fenêtre glissante pour faire face à des mesures asynchrones. Les méthodes par intervalles développées ont pour but de calculer les couloirs de confiance des variables position et vitesse en se basant sur la combinaison d’intervalles issus des capteurs d’une part et sur l’utilisation conjointe d’un modèle dynamique et cinématique du véhicule d’autre part. Dans le cadre des capteurs logiciels pour bioprocédés, trois familles de méthodes ont été étudiées: le filtrage particulaire, les méthodes par intervalles et le filtrage par horizon glissant. Le filtrage particulaire est basé sur des méthodes de Monte-Carlo pour estimer la densité de probabilité conditionnelle de l’état connaissant les mesures. Un de ses principaux inconvénients est sa sensibilité aux erreurs paramétriques. La méthode développée s’applique aux bioprocédés et profite de la structure particulière des modèles pour proposer une version du filtrage particulaire robuste aux incertitudes des paramètres cinétiques. Des méthodes d’estimation par intervalles sont adaptées à la situation où les mesures sont disponibles à des instants discrets, avec une faible fréquence d’échantillonnage, en développant des prédicteurs appropriés. L’utilisation d’un faisceau de prédicteurs grâce à des transformations d’état et le couplage entre les prédicteurs avec des réinitialisations fréquentes permettent d’améliorer les résultats d’estimation. Enfin, une méthode basée sur le filtre à horizon glissant est étudiée en effectuant une optimisation min-max : la meilleure condition initiale est reconstruite pour le plus mauvais modèle. Des solutions sont aussi proposées pour minimiser la quantité de calculs. Pour conclure, les méthodes et résultats obtenus constituent un ensemble d’améliorations dans le cadre de la mise au point d’algorithmes robustes vis-à-vis des incertitudes. Selon les applications et les objectifs fixés, telle ou telle famille de méthodes sera privilégiée. Cependant, dans un souci de robustesse, il est souvent utile de fournir les estimations sous forme d’intervalles auxquels est associé un niveau de confiance lié aux conditions de l’estimation. C’est pourquoi, une des méthodes les plus adaptées aux objectifs de robustesse est représentée par les méthodes par intervalles de confiance et leur développement constituera un point de recherche futur. __________________________________________ Abstract : This thesis work is about the synthesis of robust state estimation methods applied to two different domains. The first area is dedicated to the safe positioning in transport. The objective is to compute the vehicle position and velocity by intervals with a great confidence level. The second area is devoted to the software sensor design in bioprocess applications. The component concentrations are estimated from a limited number of measurements and a mathematical model describing the dynamical behavior of the system. The main interest is to design algorithms which achieve estimation performance and take uncertainties into account coming from the model parameters and the measurement errors. In this context, several algorithms have been studied and designed. Concerning the vehicle positioning, the research activities have led to robust Hinfinity methods and interval estimation methods. The robust Hinfinity methods use a linear model taking model uncertainty into account and perform a min-max optimization, minimizing a cost function which describes the worst-case configuration. The contribution in this domain is an extension to some systems with a nonlinear model and the use of a receding time window facing with asynchronous data. The developed interval algorithms compute confidence intervals of the vehicle velocity and position. They use interval combinations by union and intersection operations obtained from sensors along with kinematic and dynamic models. In the context of bioprocesses, three families of state estimation methods have been investigated: particle filtering, interval methods and moving-horizon filtering. The particle filtering is based on Monte-Carlo drawings to estimate the posterior probability density function of the state variables knowing the measurements. A major drawback is its sensitivity to model uncertainties. The proposed algorithm is dedicated to bioprocess applications and takes advantage of the characteristic structure of the models to design an alternative version of the particle filter which is robust to uncertainties in the kinetic terms. Moreover, interval observers are designed in the context of bioprocesses. The objective is to extend the existing methods to discrete-time measurements by developing interval predictors. The use of a bundle of interval predictors thanks to state transformations and the use of the predictor coupling with reinitializations improve significantly the estimation performance. Finally, a moving-horizon filter is designed, based on a min-max optimization problem. The best initial conditions are generated from the model using the worst parameter configuration. Furthermore, additional solutions have been provided to reduce the computational cost. To conclude, the developed algorithms and related results can be seen as improvements in the design of estimation methods which are robust to uncertainties. According to the application and the objectives, a family may be favored. However, in order to satisfy some robustness criteria, an interval is preferred along with a measure of the confidence level describing the conditions of the estimation. That is why, the development of confidence interval observers represents an important topic in the future fields of investigation.