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Autonomous navigation has the opportunity to make roads safer and help perform search and rescue missions by reducing human error. Odometry methods are essential to allow for autonomous navigation because they estimate how the robot will move based on the available sensors. This thesis aims to compare and evaluate the Cubature Kalman filter (CKF) based approach for visual-inertial odometry (VIO) to traditional Extended Kalman Filter (EKF) based methods on criteria such as the accuracy of the results. VIO methods use camera and IMU sensor for the predictions. The Multi-State-Constraint Kalman filter (MSCKF) was utilized as the foundation VIO approach to evaluate the underlying filter between EKF and CKF while maintaining the background conditions like visual tracking pipeline, IMU model, and measurement model constant. Evaluation metrics of absolute trajectory error (ATE) and relative error (RE) was used after tuning the filters on EuRoC and KAIST datasets. It is shown that, based on the existing implementation, the filters have no statistically significant difference in performance when predicting motion estimates, despite the fact that the absolute trajectory error of position for EKF estimation is lower. It is further shown that as the length of the trajectory increases, the estimation error for both filters rises unboundedly. Under the visual inertial framework of MSCKF, the CKF filter, which does not linearize the system, works equally as well as the well-established EKF filter and has the potential to perform better with more accurate nonlinear system and measurement models. / Autonom navigering har möjlighet att göra vägar säkrare och hjälpa till att utföra räddningsuppdrag genom att minska mänskliga fel. Odometrimetoder är viktiga för att möjliggöra autonom navigering eftersom de skattar hur roboten rör sig baserat på tillgängliga sensorer. Detta examensarbete syftar till att utvärdera Cubature Kalman filter (CKF) för visuell tröghetsodometri (VIO) och jämföra med traditionella Extended Kalman Filter (EKF) gällande bland annat noggrannhet. VIO-metoder använder kamera och IMU-sensor för skattningarna. MultiState Constraint Kalmanfiltret (MSCKF) användes som grund VIO-metoden för att utvärdera filteralgoritmerna EKF och CKF, samtidigt som de VIO-specifika delarna så som IMU-modell och mätmodell kunde förbli desamma. Utvärderingen gjordes baserat på absolut banfel (ATE) och relativa fel (RE) på EuRoC- och KAIST-datauppsättningar. Det visas att, baserat på den befintliga implementeringen, har filtren ingen statistiskt signifikant skillnad i prestanda när de förutsäger rörelsen, trots att det absoluta banafelet för positionen för EKF-uppskattning är lägre. Det visas vidare att när längden på banan ökar, ökar uppskattningsfelet för båda filtren obegränsat. Under MSCKFs visuella tröghetsramverk fungerar CKF-filtret, som inte linjäriserar systemet, lika bra som det väletablerade EKF-filtret och har potential att prestera bättre med mer exakta olinjära system och mätmodeller.

The thesis work investigates brake friction estimation strategies. The friction between the brake disc and brake pads is not constant during the braking application and contributes to the amount of brake torque achieved at the wheels. In this study, it is considered that any change in the brake torque between the requested and achieved values is only due to the varying brake friction coefficient. The work gives three different approaches to estimate the brake friction coefficient using two prominent state estimation strategies, Unscented Kalman Filter and Moving Horizon Estimation. The inputs to the estimators are obtained from a Vehicle model, which is built using the wheel balance equations. The estimators have been tuned to minimize the estimation error in nominal conditions and tested for their robustness through a wide analysis, where the sensitivity of the strategies is checked against a spectra of potential system parameters and boundary conditions. Throughout all the analysis, the developed models estimate the brake friction coefficient within an acceptable error range. This work opens up opportunities for further studies that can be performed using the built estimator models. / Detta examensarbete studerar strategier för skattning av bromsfriktion. Friktionen mellan bromsskivan och bromsbeläggen är inte konstant under bromsförloppet och det är denna som genererar bromsmomentet för varje hjul. I detta arbete så antas att förändringen i bromsmoment mellan begärd och uppnått endast är på grund av varierande bromsfriktion mellan bromsbelägg och bromsskiva. Arbetet presenterar tre olika sätt att skatta bromsfriktionen genom användning av två kända skattningsmetoder, Uncented Kalman Filter och Moving Horizon Estimation. Ingående värden till skattningsmetoderna fås från en fordonsmodell som är byggd med hjälp av hjulbalansekvationer. Skattningsmetoderna har justerats så att de minimerar skattningsfelet i nominella fall och de är testade för robusthet genom en bred analys där känsligheten hos metoderna testas genom en flora av potentiella systemparametrar och gränsvärden. Genom hela analysen så uppnår de utvecklade skattningsmetoderna bromsfriktionsvärden med acceptabla felnivåer. Detta arbete öppnar upp för möjligheter för vidare analyser där de utvecklade metoderna kan användas.

The Integrated Vehicle Dynamics Control (IVDC) concept can influence the vehicle behaviour both longitudinally and laterally with just one upper level control concept and further lower level controllers. This demands for state estimation of the vehicle which also includes estimating parameters of interest for the vehicle dynamicist. The approach to this research is firstly in developing a robust unscented Kalman filter (UKF) estimator for the vehicle side slip tracking and also for cornering stiffness estimation which is then fed to the existing model predictive control allocation (MPCA) controller to enhance the lateral stability of the vehicle for the different manoeuvres studied. Based on these developments, two types of filters are created. One with adaption of distance between center of gravity (COG) and roll center height and another without adaption. The key factor in the estimator development is the time adaptive process covariance matrix for the cornering stiffnesses, with which only the initial values have to be parameterised. Combining this research encompasses effective and adaptive method for a better quality of estimation with a kinematic vehicle model which behaves like a real world vehicle, at least virtually.This study is carried out with the understanding of various optimal estimators, parametric sensitivity analysis and statistical inferences, facilitating a base for robust estimation. Keywords: kalametric, state estimation, design matrix, aliasing, kalman filter, projection algorithm, resolution / Konceptet Integrated Vehicle Dynamics Control (IVDC) kan påverka fordonets beteende både longitudinellt och lateralt med bara ett regler koncept iett övre lager och ytterligare regulatorer på lägre nivåer. Detta kräver tillståndsuppskattning av fordonet som också inkluderar uppskattning av parametrar av intresse för en fordonsdynamiker. Tillvägagångssättet för denna studie är för det första att utveckla en robust tillståndsestimering med hjälp av ett Unscented Kalman Filter (UKF) för att uppskatta ett fordons avdriftsvinkel och även för uppskattning av ett däcks sidkraftskoefficient, vilket sedan används i den befintliga modell-prediktiva regleralgoritmen (MPCA) för att förbättra lateralstabiliteten hos fordonet för de olika studerade manövrarna. Baserat på denna utveckling skapades två typer av filter, ett med anpassning av avståndet mellan tyngdpunkten (COG) och krängcentrumhöjden och ett annat utan anpassning. Nyckelfaktorn i estimeringsutvecklingen är den tidsberoende adaptiva inställningenav processkovariansmatrisen för sidkraftskoefficienterna, med vilken endast de initiala värdena behöver parametriseras. Efter filterutvecklingen identifieras parametrar baserade på en förväntad kundanvändning och en statistisk variansanalys (ANOVA) utförs för att bestämma de mest inflytelserika faktorerna i gruppen. En parameteroptimering utförs för att förbättra uppskattningskvaliteten. Kombinationen av detta arbete omfattar en effektiv och anpassningsbar metod för en bättre uppskattningskvalitet med en kinematisk fordonsmodell som har en fordonsrespons som ett verkligt fordon, åtminstone praktiskt taget. Denna studie har genomförts med förståelse för olika optimala estimatorer, parametrisk känslighetsanalys och statistiska slutsatser, vilket underlättaren bas för robust uppskattning. Nyckelord: kalametric, tillståndsestimering, designmatris, vikningsdistorsion, kalmanfilter,projection algorithm, upplösning

Abstract: The interest of major space agencies in the world for vision sensors in their mission designs has been increasing over the years. Indeed, cameras offer an efficient solution to address the ever-increasing requirements in performance. In addition, these sensors are multipurpose, lightweight, proven and a low-cost technology. Several researchers in vision sensing for space application currently focuse on the navigation system for autonomous pin-point planetary landing and for sample and return missions to small bodies. In fact, without a Global Positioning System (GPS) or radio beacon around celestial bodies, high-accuracy navigation around them is a complex task. Most of the navigation systems are based only on accurate initialization of the states and on the integration of the acceleration and the angular rate measurements from an Inertial Measurement Unit (IMU). This strategy can track very accurately sudden motions of short duration, but their estimate diverges in time and leads normally to high landing error. In order to improve navigation accuracy, many authors have proposed to fuse those IMU measurements with vision measurements using state estimators, such as Kalman filters. The first proposed vision-based navigation approach relies on feature tracking between sequences of images taken in real time during orbiting and/or landing operations. In that case, image features are image pixels that have a high probability of being recognized between images taken from different camera locations. By detecting and tracking these features through a sequence of images, the relative motion of the spacecraft can be determined. This technique, referred to as Terrain-Relative Relative Navigation (TRRN), relies on relatively simple, robust and well-developed image processing techniques. It allows the determination of the relative motion (velocity) of the spacecraft. Despite the fact that this technology has been demonstrated with space qualified hardware, its gain in accuracy remains limited since the spacecraft absolute position is not observable from the vision measurements. The vision-based navigation techniques currently studied consist in identifying features and in mapping them into an on-board cartographic database indexed by an absolute coordinate system, thereby providing absolute position determination. This technique, referred to as Terrain-Relative Absolute Navigation (TRAN), relies on very complex Image Processing Software (IPS) having an obvious lack of robustness. In fact, these software depend often on the spacecraft attitude and position, they are sensitive to illumination conditions (the elevation and azimuth of the Sun when the geo-referenced database is built must be similar to the ones present during mission), they are greatly influenced by the image noise and finally they hardly manage multiple varieties of terrain seen during the same mission (the spacecraft can fly over plain zone as well as mountainous regions, the images may contain old craters with noisy rims as well as young crater with clean rims and so on). At this moment, no real-time hardware-in-the-loop experiment has been conducted to demonstrate the applicability of this technology to space mission. The main objective of the current study is to develop autonomous vision-based navigation algorithms that provide absolute position and surface-relative velocity during the proximity operations of a planetary mission (orbiting phase and landing phase) using a combined approach of TRRN and TRAN technologies. The contributions of the study are: (1) reference mission definition, (2) advancements in the TRAN theory (image processing as well as state estimation) and (3) practical implementation of vision-based navigation. / Résumé: L’intérêt des principales agences spatiales envers les technologies basées sur la vision artificielle ne cesse de croître. En effet, les caméras offrent une solution efficace pour répondre aux exigences de performance, toujours plus élevées, des missions spatiales. De surcroît, ces capteurs sont multi-usages, légers, éprouvés et peu coûteux. Plusieurs chercheurs dans le domaine de la vision artificielle se concentrent actuellement sur les systèmes autonomes pour l’atterrissage de précision sur des planètes et sur les missions d’échantillonnage sur des astéroïdes. En effet, sans système de positionnement global « Global Positioning System (GPS) » ou de balises radio autour de ces corps célestes, la navigation de précision est une tâche très complexe. La plupart des systèmes de navigation sont basés seulement sur l’intégration des mesures provenant d’une centrale inertielle. Cette stratégie peut être utilisée pour suivre les mouvements du véhicule spatial seulement sur une courte durée, car les données estimées divergent rapidement. Dans le but d’améliorer la précision de la navigation, plusieurs auteurs ont proposé de fusionner les mesures provenant de la centrale inertielle avec des mesures d’images du terrain. Les premiers algorithmes de navigation utilisant l’imagerie du terrain qui ont été proposés reposent sur l’extraction et le suivi de traits caractéristiques dans une séquence d’images prises en temps réel pendant les phases d’orbite et/ou d’atterrissage de la mission. Dans ce cas, les traits caractéristiques de l’image correspondent à des pixels ayant une forte probabilité d’être reconnus entre des images prises avec différentes positions de caméra. En détectant et en suivant ces traits caractéristiques, le déplacement relatif du véhicule (la vitesse) peut être déterminé. Ces techniques, nommées navigation relative, utilisent des algorithmes de traitement d’images robustes, faciles à implémenter et bien développés. Bien que cette technologie a été éprouvée sur du matériel de qualité spatiale, le gain en précision demeure limité étant donné que la position absolue du véhicule n’est pas observable dans les mesures extraites de l’image. Les techniques de navigation basées sur la vision artificielle actuellement étudiées consistent à identifier des traits caractéristiques dans l’image pour les apparier avec ceux contenus dans une base de données géo-référencées de manière à fournir une mesure de position absolue au filtre de navigation. Cependant, cette technique, nommée navigation absolue, implique l’utilisation d’algorithmes de traitement d’images très complexes souffrant pour le moment des problèmes de robustesse. En effet, ces algorithmes dépendent souvent de la position et de l’attitude du véhicule. Ils sont très sensibles aux conditions d’illuminations (l’élévation et l’azimut du Soleil présents lorsque la base de données géo-référencée est construite doit être similaire à ceux observés pendant la mission). Ils sont grandement influencés par le bruit dans l’image et enfin ils supportent mal les multiples variétés de terrain rencontrées pendant la même mission (le véhicule peut survoler autant des zones de plaine que des régions montagneuses, les images peuvent contenir des vieux cratères avec des contours flous aussi bien que des cratères jeunes avec des contours bien définis, etc.). De plus, actuellement, aucune expérimentation en temps réel et sur du matériel de qualité spatiale n’a été réalisée pour démontrer l’applicabilité de cette technologie pour les missions spatiales. Par conséquent, l’objectif principal de ce projet de recherche est de développer un système de navigation autonome par imagerie du terrain qui fournit la position absolue et la vitesse relative au terrain d’un véhicule spatial pendant les opérations à basse altitude sur une planète. Les contributions de ce travail sont : (1) la définition d’une mission de référence, (2) l’avancement de la théorie de la navigation par imagerie du terrain (algorithmes de traitement d’images et estimation d’états) et (3) implémentation pratique de cette technologie.

Cette thèse contribue à répondre au problème de la gestion optimale des ressources en eau dans les réseaux de navigation intérieure du point de vue de la théorie du contrôle. Les objectifs principales à atteindre consistent à garantir la navigabilité des réseaux de voies navigables, veiller à la réduction des coûts opérationnels et à la longue durée de vie des équipements. Lors de la conception de lois de contrôle, les caractéristiques des réseaux doivent être prises en compte, à savoir leurs dynamiques complexes, des retards variables et l’absence de pente. Afin de réaliser la gestion optimale, le contrôle efficace des structures hydrauliques doit être assuré. A cette fin, une approche de modélisation orientée contrôle est dérivée. Cependant, la formulation obtenue appartient à la classe des systèmes de descripteurs retardés, pour lesquels la commande prédictive MPC et l’estimation d’état sur horizon glissant MHE peuvent être facilement adaptés à cette formulation, tout en permettant de gérer les contraintes physiques et opérationnelles de manière naturelle. En raison de leur grande dimensionnalité, une mise en œuvre centralisée n’est souvent ni possible ni souhaitable. Compte tenu du fait que les réseaux de navigation intérieure sont des systèmes fortement couplés, une approche distribuée est proposée, incluant un protocole de communication entre agents. Malgré l’optimalité des solutions, toute erreur peut entraîner une gestion inefficace du système. Par conséquent, les dernières contributions de la thèse concernent la conception de stratégies de supervision permettant de détecter et d’isoler les pannes des équipements. Toutes les approches présentées sont appliquées à une étude de cas réaliste basée sur le réseau de voies navigables du nord e la France afin de valider leur efficacité. / This thesis addresses the problem of optimal management of water resources in inland navigation networks from a control theory perspective. The main objectives to be attained consist in guaranteeing the navigability condition of the network, minimizing the operational cost and ensuring a long lifespan of the equipment. However, their complex dynamics, large time delays and negligible bottom slopes complicate their management. In order to achieve the optimal management, the efficient control of the hydraulic structures must be ensured. To this end, a control-oriented modeling approach is derived. The resulting formulation belongs to the class of delayed desciptor systems, for which model predictive control and moving horizon estimation can be easily adapted, as well as being able to deal with physical and operational constraints in a natural manner. However, a centralized implementation is often neither possible nor desirable. As these networks are strongly coupled systems, a distributed approach is followed, featuring a communication protocol among agents. Despite the optimality of the solutions, any malfunction can lead to an inefficient system management. Therefore, the last part of the thesis regards the design of supervisory strategies that allow to detect and isolate faults. All the presented approaches are applied to a realistic case study based on the inland navigation network in the north of France to validate their effectiveness.

A partir de uma análise geométrica, do estimador de estado por mínimos quadrados ponderados, propõe-se, neste trabalho, um índice para classificação das medidas de acordo com as suas características de não refletirem grande parcela de seus erros nos resíduos do processo de estimação de estado, por mínimos quadrados ponderados. O índice proposto foi denominado Índice de Não-Detecção de Erros, ou apenas UI (Undetectability Index). As medidas com maiores UI são aquelas cujos erros grosseiros (EGs) são mais difíceis de serem detectados através de métodos que fazem uso da análise dos resíduos. Isto porque os erros dessas medidas são, de certa forma, \"mascarados\", isto é, não são refletidos nos resíduos das mesmas. Nesse sentido, a medida crítica é o caso limite de medidas cujos erros são mascarados, isto é, possui UI infinito e seu resíduo é igual a zero, independente de a mesma ter ou não EG. Para obtenção dos índices UI das medidas, desenvolveu-se um algoritmo simples e de fácil implementação. Tomando por base o índice UI, propõe-se, também, uma metodologia para processamento de EGs e dois algoritmos para projeto ou fortalecimento de sistemas de medição. Esses algoritmos possibilitam a obtenção de sistemas de medição confiáveis (observáveis e isentos de medidas críticas e de conjuntos críticas de medidas), de baixo custo e formados por medidas com índices UI menores que um valor pré-estabelecido. Para validar o índice UI e as suas aplicações propostas neste trabalho, realizaram-se diversas simulações computacionais nos sistemas de 14 e 30 barras do IEEE, tendo sido satisfatórios todos os resultados obtidos. / The present thesis proposes an index, called Undetectability Index (UI), to classify the measurements according to their characteristics of not reflecting their errors into the residuals of the weighted least squares state estimation process from a geometric analysis of this estimator. Gross errors in measurements with higher UIs are very difficult to be detected by methods based on the residual analysis, as the errors in those measurements are \"masked\", i.e., they are not reflected in the residuals. In this sense, critical measurements are the limit case of measurements that mask errors, that is, they have infinite UI and their residuals are always zero independently of their having or not gross errors. Based on the UI a methodology for gross error processing and two algorithms for metering system planning are also proposed in this thesis. These algorithms enable the obtaining of reliable measurement systems (observable and free from critical measurements and critical sets of measurements) with low investment and containing only measurements with UIs lower than a pre-established value. Several simulation results (with IEEE 14-bus and 30-bus systems) have validated the UI and its application.

Esta dissertação trata do problema de processamento de Erros Grosseiros (EGs) com base na aplicação do chamado Índice de Não-Detecção de Erros, ou apenas UI (Undetectability Index), na análise dos resíduos do estimador de estado por mínimos quadrados ponderados. O índice UI foi desenvolvido recentemente e possibilita a classificação das medidas de acordo com as suas características de não refletirem grande parcela de seus erros nos resíduos daquele estimador. As medidas com maiores UIs são aquelas cujos erros são mais difíceis de serem detectados através de métodos que fazem uso da análise dos resíduos, pois grande parcela do erro dessas medidas não aparece no resíduo. Inicialmente demonstrou-se, nesta dissertação, que erros das estimativas das variáveis de estado em um sistema com EG não-detectável (em uma medida de alto índice UI) podem ser mais significativos que em medidas com EGs detectáveis (em medidas com índices UIs baixos). Justificando, dessa forma, a importância de estudos para tornar possível o processamento de EGs em medidas com alto índice UI. Realizou-se, então, nesta dissertação, diversas simulações computacionais buscando analisar a influência de diferentes ponderações de medidas no UI e também nos erros das estimativas das variáveis de estado. Encontrou-se, então, uma maneira que destacou-se como a mais adequada para ponderação das medidas. Por fim, ampliaram-se, nesta dissertação, as pesquisas referentes ao UI para um estimador de estado por mínimos quadrados ponderados híbrido. / This dissertation deals with the problem of Gross Errors processing based on the use of the so-called Undetectability Index, or just UI. This index was developed recently and it is capable to classify the measurements according to their characteristics of not reflecting their errors into the residuals of the weighted least squares state estimation process. Gross errors in measurements with higher UIs are very difficult to be detected by methods based on the residual analysis, as the errors in those measurements are masked, i.e., they are not reflected in the residuals. Initially, this dissertation demonstrates that a non-detectable gross error (error in a measurement with high UI) may affect more the accuracy of the estimated state variables than a detectable gross error (error in a measurement with low UI). Therefore, justifying the importance of studies that make possible gross errors processing in measurements with high UI. In this dissertation, several computational simulations are carried out to analyze the influence of different weights of measurements in the UI index and also in the accuracy of the estimated state variables. It is chosen a way that stood out as the most appropriate for weighing the measurements. Finally, in this dissertation, the studies referring to the UI is extended for a hybrid weighted least squares state estimator.

A partir de uma análise geométrica, do estimador de estado por mínimos quadrados ponderados, propõe-se, neste trabalho, um índice para classificação das medidas de acordo com as suas características de não refletirem grande parcela de seus erros nos resíduos do processo de estimação de estado, por mínimos quadrados ponderados. O índice proposto foi denominado Índice de Não-Detecção de Erros, ou apenas UI (Undetectability Index). As medidas com maiores UI são aquelas cujos erros grosseiros (EGs) são mais difíceis de serem detectados através de métodos que fazem uso da análise dos resíduos. Isto porque os erros dessas medidas são, de certa forma, \"mascarados\", isto é, não são refletidos nos resíduos das mesmas. Nesse sentido, a medida crítica é o caso limite de medidas cujos erros são mascarados, isto é, possui UI infinito e seu resíduo é igual a zero, independente de a mesma ter ou não EG. Para obtenção dos índices UI das medidas, desenvolveu-se um algoritmo simples e de fácil implementação. Tomando por base o índice UI, propõe-se, também, uma metodologia para processamento de EGs e dois algoritmos para projeto ou fortalecimento de sistemas de medição. Esses algoritmos possibilitam a obtenção de sistemas de medição confiáveis (observáveis e isentos de medidas críticas e de conjuntos críticas de medidas), de baixo custo e formados por medidas com índices UI menores que um valor pré-estabelecido. Para validar o índice UI e as suas aplicações propostas neste trabalho, realizaram-se diversas simulações computacionais nos sistemas de 14 e 30 barras do IEEE, tendo sido satisfatórios todos os resultados obtidos. / The present thesis proposes an index, called Undetectability Index (UI), to classify the measurements according to their characteristics of not reflecting their errors into the residuals of the weighted least squares state estimation process from a geometric analysis of this estimator. Gross errors in measurements with higher UIs are very difficult to be detected by methods based on the residual analysis, as the errors in those measurements are \"masked\", i.e., they are not reflected in the residuals. In this sense, critical measurements are the limit case of measurements that mask errors, that is, they have infinite UI and their residuals are always zero independently of their having or not gross errors. Based on the UI a methodology for gross error processing and two algorithms for metering system planning are also proposed in this thesis. These algorithms enable the obtaining of reliable measurement systems (observable and free from critical measurements and critical sets of measurements) with low investment and containing only measurements with UIs lower than a pre-established value. Several simulation results (with IEEE 14-bus and 30-bus systems) have validated the UI and its application.