Apresenta-se o desenvolvimento de modelos matemáticos e algoritmos de fusão sensorial para navegação terrestre usando uma unidade de medição inercial (UMI) de baixo desempenho e o Filtro Estendido de Kalman. Os modelos foram desenvolvidos com base nos sistemas de navegação inercial strapdown (SNIS). O termo baixo desempenho refere-se à UMIs que por si só não são capazes de efetuar o auto- alinhamento por girocompassing. A incapacidade de se navegar utilizando apenas uma UMI de baixo desempenho motiva a investigação de técnicas que permitam aumentar o grau de precisão do SNIS com a utilização de sensores adicionais. Esta tese descreve o desenvolvimento do modelo completo de uma fusão sensorial para a navegação inercial de um veículo terrestre usando uma UMI de baixo desempenho, um hodômetro e uma bússola eletrônica. Marcas topográficas (landmarks) foram instaladas ao longo da trajetória de teste para se medir o erro da estimativa de posição nesses pontos. Apresenta-se o desenvolvimento do Filtro de Kalman Adaptativo Suavizado (FKAS), que estima conjuntamente os estados e o erro dos estados estimados do sistema de fusão sensorial. Descreve-se um critério quantitativo que emprega as incertezas de posição estimadas pelo FKAS para se determinar a priori, dado os sensores disponíveis, o intervalo de tempo máximo que se pode navegar dentro de uma margem de confiabilidade desejada. Conjuntos reduzidos de landmarks são utilizados como sensores fictícios para testar o critério de confiabilidade proposto. Destacam-se ainda os modelos matemáticos aplicados à navegação terrestre, unificados neste trabalho. Os resultados obtidos mostram que, contando somente com os sensores inerciais de baixo desempenho, a navegação terrestre torna-se inviável após algumas dezenas de segundos. Usando os mesmos sensores inerciais, a fusão sensorial produziu resultados muito superiores, permitindo reconstruir trajetórias com deslocamentos da ordem de 2,7 km (ou 15 minutos) com erro final de estimativa de posição da ordem de 3 m. / This work presents the development of the mathematical models and the algorithms of a sensor fusion system for terrestrial navigation using a low-grade inertial measurement unit (IMU) and the Extended Kalman Filter. The models were developed on the basis of the strapdown inertial navigation systems (SINS). Low-grade designates an IMU that is not able to perform girocompassing self-alignment. The impossibility of navigating relying on a low performance IMU is the motivation for investigating techniques to improve the SINS accuracy with the use of additional sensors. This thesis describes the development of a comprehensive model of a sensor fusion for the inertial navigation of a ground vehicle using a low-grade IMU, an odometer and an electronic compass. Landmarks were placed along the test trajectory in order to allow the measurement of the error of the position estimation at these points. It is presented the development of the Smoothed Adaptive Kalman Filter (SAKF), which jointly estimates the states and the errors of the estimated states of the sensor fusion system. It is presented a quantitative criteria which employs the position uncertainties estimated by SAKF in order to determine - given the available sensors, the maximum time interval that one can navigate within a desired reliability. Reduced sets of landmarks are used as fictitious sensors to test the proposed reliability criterion. Also noteworthy are the mathematical models applied to terrestrial navigation that were unified in this work. The results show that, only relying on the low performance inertial sensors, the terrestrial navigation becomes impracticable after few tens of seconds. Using the same inertial sensors, the sensor fusion produced far better results, allowing the reconstruction of trajectories with displacements of about 2.7 km (or 15 minutes) with a final error of position estimation of about 3 m.

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Les travaux présentés dans ce mémoire de thèse concernent le développement et la mise en oeuvre d'un algorithme de filtrage particulaire pour le recalage de la navigation inertielle par mesures altimétriques. Le filtre développé, le MRPF (Mixture Regularized Particle Filter), s'appuie à la fois sur la modélisation de la densité a posteriori sous forme de mélange fini, sur le filtre particulaire régularisé ainsi que sur l'algorithme mean-shift clustering. Nous proposons également une extension du MRPF au filtre particulaire Rao-Blackwellisé appelée MRBPF (Mixture Rao-Blackwellized Particle Filter). L'objectif est de proposer un filtre adapté à la gestion des multimodalités dues aux ambiguïtés de terrain. L'utilisation des modèles de mélange fini permet d'introduire un algorithme d'échantillonnage d'importance afin de générer les particules dans les zones d'intérêt. Un second axe de recherche concerne la mise au point d'outils de contrôle d'intégrité de la solution particulaire. En nous appuyant sur la théorie de la détection de changement, nous proposons un algorithme de détection séquentielle de la divergence du filtre. Les performances du MRPF, MRBPF, et du test d'intégrité sont évaluées sur plusieurs scénarios de recalage altimétrique / This thesis deals with the development of a mixture particle filtering algorithm for inertial navigation update via radar-altimeter measurements. This particle filter, the so-called MRPF (Mixture Regularized Particle Filter), combines mixture modelling of the posterior density, the regularized particle filter and the mean-shift clustering algorithm. A version adapted to the Rao-Blackwellized particle filter, the MRBPF (Mixture Rao-Blackwellized Particle Filter), is also presented. The main goal is to design a filter well suited to multimodal densities caused by terrain amibiguity. The use of mixture models enables us to introduce an alternative importance sampling procedure aimed at proposing samples in the high likelihood regions of the state space. A second research axis is concerned with the development of particle filtering integrity monitoring tools. A novel particle filter divergence sequential detector, based on change detection theory, is presented. The performances of the MRPF, MRBPF and the divergence detector are reported on several terrain navigation scenarios

This thesis deals with inertial navigation systems issues. It describes basics of reference frames, coordinate systems and matrix calculations for AHRS. There are also basic information about inertial sensors, inertial measurements units and its mistakes. One of the purposes of this paper could be explanation of inertial navigation systems terms. The main object of this thesis is to explore the influence of using multiple sensors of same type to enhance measurements of AHRS systems.

Diplomová práce se zabývá návrhem algoritmu rozšířeného Kalmanova filtru, který integruje data z inerciálního navigačního systému (INS) a globálního polohovacího systému (GPS). Součástí algoritmu je i samotná mechanizace INS, určující na základě dat z akcelerometrů a gyroskopů údaje o rychlosti, zeměpisné pozici a polohových úhlech letadla. Vzhledem k rychlému nárůstu chybovosti INS je výstup korigován hodnotami rychlosti a pozice získané z GPS. Výsledný algoritmus je implementován v prostředí Simulink. Součástí práce je odvození jednotlivých stavových matic rozšířeného Kalmanova filtru.

Für viele Anwendungen auf bewegten Plattformen wird eine genaue Information zur Orientierung der Plattform benötigt. Zur Bestimmung der Lagewinkel werden dabei inertiale Messsensoren verwendet, welche zu einer inertialen Messeinheit (Inertial Measurement Unit, IMU) zusammengefasst werden. In dieser Arbeit werden vier IMUs aus dem Niedrigpreissegment auf die zu erhaltene Genauigkeit der Lagewinkel untersucht. Die untersuchten IMUs sind dabei als Mikrosysteme (Microelectromechanical systems) gefertigt, was neben den Vorteilen eines geringen Preises, eines geringen Gewichts und eines geringen Energieverbrauchs allerdings auch den Nachteil einer schlechteren Genauigkeit gegenüber klassischen IMUs hat. In dieser Arbeit wird die Genauigkeitsuntersuchung anhand eines Datensatzes einer Flugkampagne durchgeführt, für welche auch eine Referenzlösung vorliegt. Die Messungen der IMUs werden über ein Erweitertes Kalman-Filter mit einer genauen GNSS- (Global Navigation Satellite System) Lösung gestützt. Neben der Navigationslösung werden dabei auch die Fehler der Sensoren mitgeschätzt. Aufgrund von zu großen Fehlern der Startwerte kommt es bei einigen Schätzungen teilweise zur Divergenz. Zur Lösung dieses Problems wird eine iterative Auswertung angewendet, wodurch eine stabile Lösung möglich ist. Eine weitere Verbesserung wird über eine Glättung erzielt. Einzelne, kleine Fehler in der Zeitstempelung, welche sich stark auf die Genauigkeit der Lösung auswirken, werden über eine Interpolation der Daten auf Zeitstempel in regelmäßigen Abständen ausgeglichen. Damit können für zwei der vier untersuchten IMUs auf den Fluglinien Genauigkeiten der Roll-, Pitch- und Yaw-Winkel von 0,05°, 0,10° und 0,20° erreicht werden. Die Genauigkeiten der zwei weiteren IMUs fallen teilweise erheblich schlechter aus, was auf die ungenaue Zeitstempelung bei der Datenaufnahme zurückgeführt wird. Für die Anwendung von Laserscanning auf bewegten Plattformen wird in einer Genauigkeitsabschätzung gezeigt, dass Genauigkeiten der Höhenkomponente von besser als 1 dm mit den erhaltenen Lagewinkelgenauigkeiten der beiden besseren IMUs möglich sind.

Indoor Positional Navigation (IPN) systems can be used to track the position of tools in factories which is crucial for quality assurance in many manufacturing industries. Inertial navigation is rarely used on its own because of the noisy Inertial Measurement Unit (IMU) sensors which contribute to large drift. Current IPN systems usually involve the installation and calibration of cameras or antennas, so achieving sufficient accuracy with inertial navigation based IPN would be very desirable. This project aims to evaluate an inertial navigation algorithm, based on Zero Velocity Update (ZUPT), for bolt level positioning by repeatability tests using an industrial robot. The ZUPT algorithm, developed at Atlas Copco, manages to effectively reduce drift and achieve moderate accuracy in position for simpler movements. The gravity tracking Kalman filter dictates the systematic errors in position that grow large with increased degree and dimension of rotation. When keeping rotations within 45◦ for a linear movement the absolute error in position is under 10%. Frequent stops are important when moving in a more complex trajectory to be able to negate drift, consequently detecting the start and stop of motion is crucial. The results show that increased frequency will improve accuracy. It is shown that averaging IMU samples before calculations can increase both truthfulness and precision by 10−25%, if sampling the IMU faster than the calculations. The ZUPT approach of inertial navigation will never yield positional results in real time, and the evaluated algorithm only performs well within certain limitations, mainly frequent stops and simple movements. Despite these limitations there is potential in using the algorithm for quality assurance purposes in hand held industrial fasteners. / Kvalitetssäkring är en central fråga för många tillverkningsindustrier, så som flygplans- och bilindustrin, där det är avgörande att varje förband har dragits åt på rätt sätt för att garantera säkerheten i produkten. Moderna fabriker har centrala styrsystem som kommunicerar med maskiner och verktyg, och ifall något blir fel är det vanligt att fabrikslinan stannar vilket blir kostsamt. Inomhuspositionering (IPS) av hög noggrannhet kan spåra vilken åtdragning som blivit fel, vilket dokumenteras och åtgärdas om möjligt senare, utan att stanna fabrikslinan. Dagens noggranna IPS system för kvalitetssäkring kräver installation och kalibrering av kameror och/eller antenner. Tröghetsnavigering kräver i grunden bara billiga sensorer installerade på verktyget men metoden är mycket opålitlig på grund av sensorernas opålitlighet och brus. I detta projekt har en metod för tröghetsnavigering, användandes Zero Velocity Update (ZUPT), evaluerats för kvalitetssäkring av handhållna verktyg genom repetabilitetstester. Tröghetsnavigeringsalgoritmen som tidigare utvecklats på Atlas Copco lyckas på effektivt sätt reducera drift och uppnår rimlig noggranhet för enklare rörelser. För linjära rörelser med rotationer under 45◦ så erhålls ett absolut positionsfel inom 10%. För att fungera väl även för mer komplexa rörelser krävs frekventa stop, och noggrann rörelsedetektion är central. Denna ZUPT-metod kommer aldrig att kunna generera position i realtid och algoritmen presterar väl endast inom vissa begränsningar. Trots detta så finns god potential för metoden inom kvalitetssäkring för handhållna industriverktyg.

This thesis investigates the feasibility and performance of an inertial based positioning system for dairy cows in a barn environment. The investigated positioning method is pedestrian dead reckoning using inertial navigation with MEMS sensors. While this method is well known for human positioning applications, there has not been a lot of studies of its use on terrestrial animals. Since inertial based positioning systems are dependent on drift correction, the focus of the research is drift correction methods. Two methods, zero velocity update (ZUPT) and sparse locations, are compared with regards to positioning accuracy, energy consumption and sensor placement.  The best positioning estimates are achieved by using ZUPT corrections at a sample rate of 10 Hz, resulting in a mean position drift of 0.2145 m=m. Using a proposed equidistant sample time based sleep mode scheme, this would require a theoretical supply current of 0.21 mA. It is also seen that better position estimates are obtained for sensors that are placed low and on the front legs. The sparse locations method suffers from severe position drift between the locations, resulting in unusable positioning data. A combination of ZUPT and sparse location yields less accurate positioning than ZUPT only. / Denna masteruppsats undersöker genomförbarhet och prestanda av ett tröghetsbaserat positioneringsssystem för mjölkkor i en lada. Den undersökta metoden är död räkning för fotgängare mha. tröghetsnavigering med MEMSsensorer. Denna metod är välkänd för positionering av människor, men få studier har gjorts kring dess användbarhet för djur. Eftersom tröghetsbaserad navigering är beroende av driftkorrigering är detta fokuset för forskningen. Två olika metoder utvärderas, zero velocity update (ZUPT) och sparse locations, och en jämförelse görs med avseende på positionsnoggrannhet, energiförbrukning och sensorplacering.Bäst positionering uppnås med ZUPT-korrigeringar vid en samplingsfrekvens på 10 Hz, vilket ger ett medelvärde av positionsdrift på 0.2145 m=m. Om ett föreslaget ekvidistant samplingstidsbaserat schema för viloläge används skulle 10 Hz kräva en teoretisk matningsström på 0.21 mA. Vidare fås bättre positioneringsresultat för sensorer som är placerade lågt och på frambenen. Korrektionsmetoden med sparse locations ger en svår positionsdrift mellan platserna, vilket resulterar i oanvändbar positionsdata. En kombination av ZUPT och sparse locations ger sämre precision än om endast ZUPT används, samt ökar energiförburkningen på grund av behovet av ytterligare sensorer.

La résolution d'un problème d'estimation de l'état d'un système nécessite de disposer d'un modèle régissant l'évolution des variables d'état et de mesurer de manière directe ou indirecte l'ensemble ou une partie de ces variables d'état. Les travaux exposés dans ce mémoire de thèse portent sur la problématique d'estimation en présence de méconnaissances de modèle et de pertes de capteurs. La première partie de ce travail constitue la synthèse d'un dispositif d'estimation d'état pour systèmes non linéaires. Cela consiste à sélectionner un estimateur d'état et convenablement le régler, puis à concevoir algorithmiquement, à partir d'un critère introduit pour la circonstance, une redondance matérielle visant à compenser la perte de certains capteurs. La seconde partie de ce travail porte sur la conception, à l'aide de la variance d'Allan, d'un sous-modèle permettant de compenser les incertitudes d'un modèle d'état, ce sous-modèle étant utilisable par un filtre de Kalman. Ce travail a été exploité pour tenir compte de dérives gyroscopiques dans le cadre d'une navigation inertielle hybridée avec des mesures GPS par un filtre de Kalman contraint. Les résultats obtenus, issus d'expériences sur deux trajectoires d'avion, ont montré un comportement sain et robuste de l'approche proposée / To solve the problem of estimating the state of a system, it is necessary to have at one's disposal a model governing the dynamic of the state variables and to measure directly or indirectly all or a part of these variables. The work presented in this thesis deals with the estimation issue in the presence of model uncertainties and sensor losses. The first part of this work represents the synthesis of a state estimation device for nonlinear systems. It consists in selecting a state estimator and properly tuning it. Then, thanks to a criterion introduced for the occasion, it consists in algorithmically designing a hardware redundancy aiming at compensating for some sensor losses. The second part of this work deals with the conception of a sub-model compensating for some model uncertainties. This sub-model, designed by using the Allan variance, is usable by a Kalman filter. This work has been used to take into account some gyroscopical drifts in a GPS-INS integrated navigation based on a constrained Kalman filter. The results obtained, coming from experiments on two plane trajectories, showed a safe and robust behaviour of the proposed method

In this paper, a method for determining ground plane features in a sequence of images captured by a mobile camera is presented. The hardware of the mobile system consists of a monocular camera that is mounted on an inertial measurement unit (IMU). An image processing procedure is proposed, first to extract image features and match them across consecutive image frames, and second to detect the ground plane features using a two-step algorithm. In the first step, the planar homography of the ground plane is constructed using an IMU-camera motion estimation approach. The obtained homography constraints are used to detect the most likely ground features in the sequence of images. To reject the remaining outliers, as the second step, a new plane normal vector computation approach is proposed. To obtain the normal vector of the ground plane, only three pairs of corresponding features are used for a general camera transformation. The normal-based computation approach generalizes the existing methods that are developed for specific camera transformations. Experimental results on real data validate the reliability of the proposed method. / <p>QC 20121107</p>