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251	Towards Dense Visual SLAM Pietzsch, Tobias 05 December 2011 (has links) (PDF) Visual Simultaneous Localisation and Mapping (SLAM) is concerned with simultaneously estimating the pose of a camera and a map of the environment from a sequence of images. Traditionally, sparse maps comprising isolated point features have been employed, which facilitate robust localisation but are not well suited to advanced applications. In this thesis, we present map representations that allow a more dense description of the environment. In one approach, planar features are used to represent textured planar surfaces in the scene. This model is applied within a visual SLAM framework based on the Extended Kalman Filter. We presents solutions to several challenges which arise from this approach. Simultane Lokalisierung und Kartierung SLAM Visuelles SLAM Erweiterte Realität Inverse-Tiefe-Parametrisierung Bildverarbeitung Dreidimensionales Maschinelles Sehen Stereo-Sehen Flächen-Modelle Simultaneous Localization And Mapping Visual SLAM Stereo Vision Augmented Reality Extended Kalman Filter Inverse Depth Higher-Level Features Planar Features Dense Mapping Dense Maps Direct Measurement Method Visual SLAM Evaluation ddc:004 rvk:ST 308 SLAM-Verfahren Erweiterte Realität <Informatik> Bildfolgenverarbeitung Bildverarbeitung Lokalisierung <Robotik> Dreidimensionales maschinelles Sehen
252	Contributions to the study of control for small-scale wind turbine connected to electrical microgrid with and without sensor / Contribution à l'étude des commandes avec et sans capteur d'une éolienne de faible puissance insérée dans un micro réseau électrique Al Ghossini, Hossam 23 November 2016 (has links) L'objectif de cette thèse est de proposer l'approche la plus appropriée afin de minimiser le coût d'intégration de petite éolienne dans un micro-réseau DC urbain. Une petit éolienne basé sur un machine synchrone à aimant permanent (MSAP) est considéré à étudier. Un état de l'art concernant les énergies renouvelables, micro-réseau DC, et la production d'énergie éolienne, est fait. Comme le capteur mécanique de cette structure est relativement d'un coût élevé, les différents types de contrôle pour un système de conversion éolienne sont présentés afin de choisir une structure active de conversion d'énergie et un MSAP sans capteur. Par conséquent, un estimateur de vitesse/position est nécessaire pour contrôler le système. Ainsi, les méthodes différentes proposées dans la littérature sont considérées et classifiées à étudier dans les détails, puis les plus efficaces et largement utilisés sont à vérifier dans la simulation et expérimentalement pour le système étudié. Les méthodes choisies sont: estimation de la flux de rotor avec boucle à verrouillage de phase (PLL), observateur à mode glissement (SMO), observateur de Luenberger d'ordre réduit, et filtre de Kalman étendu (EKF). Face à d'autres méthodes, l'estimateur basé sur un modèle EKF permet une commande sans capteur dans une large plage de vitesse et estime la vitesse de rotation avec une réponse rapide. Le réglage des paramètres EKF est le problème principal à sa mise en œuvre. Par conséquent, pour résoudre ce problème, la thèse présente une méthode adaptative, à savoir réglage-adaptatif d’EKF. En conséquence, et grâce à cette approche, le coût total du système de conversion est réduite et la performance est garantie et optimisée. / The aim of this thesis is to propose the most appropriate approach in order to minimize the cost of integration of a wind generator into a DC urban microgrid. A small-scale wind generator based on a permanent magnet synchronous machine (PMSM) is considered to be studied. A state of the art concerning the renewable energies, DC microgrid, and wind power generation is done. As the mechanical sensor for this structure is relatively of high cost, various types of wind conversion system control are presented in order to choose an energy conversion active structure and a sensorless PMSM. Therefore, a speed/position estimator is required to control the system. Thus, different methods proposed in literatures are considered and classified to be studied in details, and then the most effective and widely used ones are to be verified in simulation and experimentally for the studied system. The methods which are chosen are: rotor flux estimation with phase locked loop (PLL), sliding mode observer (SMO), Luenberger observer of reduced order, and extended Kalman filter (EKF). Facing to other methods, the EKF model-based estimator allows sensorless drive control in a wide speed range and estimates the rotation speed with a rapid response. The EKF parameters tuning is the main problem to its implementation. Hence, to solve this problem, the thesis introduces an adaptive method, i.e. adaptive-tuning EKF. As a result and grace to this approach, the total cost of conversion system is reduced and the performance is guaranteed and optimized. Production éolienne Éolienne à petite puissance Commande sans capteur Boucles à verrouillage de phase (PLL) Observateur à mode glissement (SMO) Filtre de Kalman étendu (EKF) Réglage adaptatif Micro-réseau DC Wind power generation DC microgrid Small-scale wind turbine Sensorless control Phased locked loop (PLL) Sliding mode observer (SMO) Reduced order Luenberger observer Extended Kalman filter (EKF) Adaptive tuning
253	Contribution au Diagnotic des Défauts de la Machine Asynchrone Doublement Alimentée de l'Eolienne à Vitesse Variable. / Fault diagnosis of a Doubly Fed Induction Generator (DFIG) in a variable speed wind turbine Idrissi, Imane 21 September 2019 (has links) Actuellement, les machines Asynchrones à Double Alimentation (MADA) sont omniprésentes dans le secteur éolien, grâce à leur simplicité de construction, leur faible coût d’achat et leur robustesse mécanique ainsi que le nombre faible d’interventions pour la maintenance. Cependant, comme toute autre machine électrique, ces génératrices sont sujettes aux défauts de différent ordre (électrique, mécanique, électromagnétique…) ou de différents types (capteur, actionneur ou composants du système). C’est pourquoi, il est primordial de concevoir une approche de diagnostic permettant de manière anticipée, de détecter, localiser et identifier tout défaut ou anomalie pouvant altérer le fonctionnement sain de ce type de machine. Motivés par les points forts des méthodes de diagnostic de défauts à base d’observateurs, nous proposons d’une part, dans cette thèse, une approche de détection, localisation et identification des défauts de la MADA d’une éolienne à vitesse variable, à base des observateurs de Kalman, performants et largement utilisés. Les erreurs d’estimation d’état du filtre de Kalman linéaire et de ses variantes non-linéaires, à noter : le Filtre de Kalman Etendu (EKF) et le Filtre de Kalman sans-Parfum (UKF), sont utilisés comme résidus sensibles aux défauts. En vue d’éviter les fausses alarmes et de découpler les défauts des perturbations et des bruits, l’analyse des résidus générés est réalisée par des tests statistiques tels que : Test de Page Hinkley (PH) et Test DCS (Dynamic Cumulative Sum). Pour la localisation des défauts multiples et simultanés, la Structure d’Observateurs Dédiés (DOS) et la Structure d’Observateurs Généralisés (GOS) sont appliquées. De plus, l’amplitude du défaut est déterminée dans l’étape d’identification de défaut. Les défauts capteurs, actionneurs et composants de la MADA, sont traités dans ce travail de recherche. D’autre part, une étude comparative entre les différents observateurs de Kalman, est élaborée. La comparaison porte sur les critères suivants : le temps de calcul, la précision et la vitesse de convergence des estimations. / Actually, the Doubly Fed Induction Generators (DFIG) are omnipresent in the wind power market, owing to their construction simplicity, their low purchase cost and their mechanical robustness. However, as any other electrical machine, these generators are subject to defects of different order (electrical, mechanical, electromagnetic ...) or of different type (sensor, actuator or system). That’s why, it is important to design an effective diagnostic approach, able to early detect, locate and identify any defect or abnormal behavior, which could undermine the healthy operation of this machine On the one hand, motivated by the observer-based fault diagnosis methods strengths, we proposed, in this thesis, a diagnostic approach for the faults detection, localization and identification of the DFIG used in variable speed wind turbine. This approach is based on the use of the efficient and widely used Kalman observers. The state estimation errors of the linear Kalman filter and the non-linear Kalman filters, named: The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are used as faults sensitive residuals. In order to avoid false alarms and to decouple faults from disturbances and noises, the faults detection is carried out by the analysis of the residuals generated, by the mean of statistical tests such as: Hinkley Page Test (PH) and DCS Test (Dynamic) Cumulative Sum). For the localization step in case of multiple and simultaneous faults, the Dedicated Observer scheme (DOS) and the Generalized Observer scheme (GOS) are applied. In addition, the fault level is determined in the fault identification step. Sensor faults, actuator and system faults of DFIG, are treated in this research work. On the other hand, a comparative study between the three Kalman observers proposed is performed. The comparison was done in terms of (1) the computation time, (2) the estimation accuracy, and (3) the convergence speed. Diagnostic de défauts Défaut capteur Filtres de Kalman Défaut composant Défaut actionneur Court-circuit inter-spires Test de Page-Hinkley (PH) Schéma d’Observateur Dédié (DOS) Filtre de Kalman Étendu (EKF), Filtre de Kalman sans parfum (UKF) Fault Diagnosis Doubly fed induction generators SensorFault Kalman Filter System Fault Actuator Fault Inter-Turn Short-Circuit Fault Page-Hinkley (PH) test Dedicated Observers Scheme (DOS), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) 621.45
254	Towards Dense Visual SLAM Pietzsch, Tobias 07 June 2011 (has links) Visual Simultaneous Localisation and Mapping (SLAM) is concerned with simultaneously estimating the pose of a camera and a map of the environment from a sequence of images. Traditionally, sparse maps comprising isolated point features have been employed, which facilitate robust localisation but are not well suited to advanced applications. In this thesis, we present map representations that allow a more dense description of the environment. In one approach, planar features are used to represent textured planar surfaces in the scene. This model is applied within a visual SLAM framework based on the Extended Kalman Filter. We presents solutions to several challenges which arise from this approach. info:eu-repo/classification/ddc/004 ddc:004
255	Fault diagnosis of lithium ion battery using multiple model adaptive estimation Sidhu, Amardeep Singh 12 1900 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / Lithium ion (Li-ion) batteries have become integral parts of our lives; they are widely used in applications like handheld consumer products, automotive systems, and power tools among others. To extract maximum output from a Li-ion battery under optimal conditions it is imperative to have access to the state of the battery under every operating condition. Faults occurring in the battery when left unchecked can lead to irreversible, and under extreme conditions, catastrophic damage. In this thesis, an adaptive fault diagnosis technique is developed for Li-ion batteries. For the purpose of fault diagnosis the battery is modeled by using lumped electrical elements under the equivalent circuit paradigm. The model takes into account much of the electro-chemical phenomenon while keeping the computational effort at the minimum. The diagnosis process consists of multiple models representing the various conditions of the battery. A bank of observers is used to estimate the output of each model; the estimated output is compared with the measurement for generating residual signals. These residuals are then used in the multiple model adaptive estimation (MMAE) technique for generating probabilities and for detecting the signature faults. The effectiveness of the fault detection and identification process is also dependent on the model uncertainties caused by the battery modeling process. The diagnosis performance is compared for both the linear and nonlinear battery models. The non-linear battery model better captures the actual system dynamics and results in considerable improvement and hence robust battery fault diagnosis in real time. Furthermore, it is shown that the non-linear battery model enables precise battery condition monitoring in different degrees of over-discharge. Fault diagnosis Kalman filter Extended Kalman filter Lithium Ion Fault probabilities Estimation theory Electronic circuits -- Testing Linear systems -- Mathematical models Electrochemical analysis -- Experiments Fault tolerance (Engineering) Electric circuit analysis Probabilities Simulation methods Thermal analysis -- Experiments Real-time control -- Experiments
256	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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