Les travaux menés durant cette thèse ont pour objectif d’améliorer les performances des systèmes hybrides GNSS/MEMS. Ils se décomposent en deux parties distinctes : d’une part, le développement d’un ensemble de traitement capteurs cherchant à améliorer la mesure elle-même et d’autre part, l’optimisation des algorithmes d’hybridation pour les capteurs MEMS de Thales. Le traitement capteur consiste en l’estimation de l’accélération vraie (resp. la vitesse angulaire vraie) à partir de la sortie du capteur accélérométrique (resp. gyrométrique). Ce traitement a été réalisé en deux sous-étapes : 1) La calibration qui consiste en l’identification du système non-linéaire connaissant ses entrées et ses sorties. Les relations entrant en jeu dans le modèle étant linéaires vis-à-vis des paramètres, on peut alors résoudre cette partie du problème par l’estimateur des moindres carrés (après extension du vecteur comprenant les entrées afin qu’il comporte les non linéarités). 2) L’inversion du modèle qui a pour but d’estimer les entrées du modèle connaissant ses sorties et l’estimation des paramètres effectuée durant l’étape de calibration. Après formalisation de ce problème sous forme d’un modèle dynamique, la résolution se fera à l’aide d’algorithme type filtre de Kalman ou filtre particulaire. Les algorithmes d’hybridation ont pour but de localiser un mobile dans l’espace connaissant l’information issue des MEMS ainsi que celle apportée par le GPS. Cette partie peut également se décomposer en deux sous-problèmes : 1) Lorsque que les signaux GPS sont disponibles (cas nominal), le but est d’améliorer les méthodes de navigation hybride GPS/INS existantes (EKF, UKF, PF, …). Dans notre cas, la réflexion a portée sur une modélisation à l’ordre 2 des biais des capteurs MEMS et sur la fermeture de la boucle de navigation (correction de la centrale inertielle à l’aide des erreurs issues du filtre d’hybridation). 2) Dans des scénarii défavorables (multitrajet et masquage des signaux GPS), la qualité des capteurs MEMS ne permet pas d’obtenir des résultats de navigation satisfaisants. Un algorithme basé sur un réseau de neurones a donc été développé. Durant les phases où le GPS est disponible, cet algorithme permet d’apprendre l’erreur commise par la centrale inertielle en mode survie par rapport au résultat de navigation hybride. Le réseau de neurones ainsi appris fournira alors cet élément de correction en cas de perte de l’information GPS. Ces différentes méthodes ont permis d’accroître la précision de la navigation GNSS/MEMS aussi bien dans le cas nominal que lors de pertes du signal GPS / The goal of this thesis is to improve accuracy of GNSS/MEMS integrated navigation system. Two main parts can be distinguished in this thesis: first, sensor processing can be achieved to improve measurement accuracy and then, navigation algorithm can be optimized for the specific case of MEMS sensors. Sensor processing is the estimation of real acceleration (resp. real angular rate) from the one measured by accelerometer (resp. gyrometer). This processing have been realized in two steps: 1) Calibration: identification of the non-linear system describing sensors (resolved by Least Square method). 2) Model inversion: estimation of the input of the non-linear system, i.e. acceleration and/or angular rate (resolved by Kalman filtering). Navigation algorithm have then to locate an object in space from both GNSS and MEMS data. This part have been also realized in two steps: 1) If GNSS signals are available, the goal is to improve the existing GNSS/INS navigation schemes (2nd-order bias modeling of MEMS sensors). 2) If GNSS are not available (e.g. multipath or outage), a Neural Network based algorithm have been developped, which learn the error made by the inertial platform during the unavailability of GNSS signals. These different methods have allowed to improve accuracy of GNSS/MEMS inetgrated navigation system both for nominal case and degraded case

No description available.

<p>Low-cost unmanned aerial vehicles (UAVs) utilizing push-broom hyperspectral scanners are poised to become a popular alternative to conventional remote sensing platforms such as manned aircraft and satellites. In order to employ this emerging technology in fields such as high-throughput phenotyping and precision agriculture, direct georeferencing of hyperspectral data using onboard integrated global navigation satellite systems (GNSS) and inertial navigation systems (INS) is required. Directly deriving the scanner position and orientation requires the spatial and rotational relationship between the coordinate systems of the GNSS/INS unit and hyperspectral scanner to be evaluated. The spatial offset (lever arm) between the scanner and GNSS/INS unit can be measured manually. However, the angular relationship (boresight angles) between the scanner and GNSS/INS coordinate systems, which is more critical for accurate generation of georeferenced products, is difficult to establish. This research presents three alternative calibration approaches to estimate the boresight angles relating hyperspectral push-broom scanner and GNSS/INS coordinate systems. For reliable/practical estimation of the boresight angles, the thesis starts with establishing the optimal/minimal flight and control/tie point configuration through a bias impact analysis starting from the point positioning equation. Then, an approximate calibration procedure utilizing tie points in overlapping scenes is presented after making some assumptions about the flight trajectory and topography of covered terrain. Next, two rigorous approaches are introduced – one using Ground Control Points (GCPs) and one using tie points. The approximate/rigorous approaches are based on enforcing the collinearity and coplanarity of the light rays connecting the perspective centers of the imaging scanner, object point, and the respective image points. To evaluate the accuracy of the proposed approaches, estimated boresight angles are used for ortho-rectification of six hyperspectral UAV datasets acquired over an agricultural field. Qualitative and quantitative evaluations of the results have shown significant improvement in the derived orthophotos to a level equivalent to the Ground Sampling Distance (GSD) of the used scanner (namely, 3-5 cm when flying at 60 m).</p>

A critical requirement for safe autonomous driving is to have an accurate state estimate of thevehicle. One of the most ubiquitous yet reliable ways for this task is through the integrationof the onboard Inertial Navigation System (INS) and the Global Navigation Satellite System(GNSS). This integration can further be assisted through fusion of information from otheronboard sensors. On top of that, a ground vehicle enforces its own set of rules, through non-holonomic constraints, which along with other vehicle dynamics can aid the state estimation.In this project, a sequential probabilistic inference approach has been followed, that fusesthe high frequency, short term accurate INS estimates, with low frequency, drift free GPSobservations. The fusion of GPS and IMU has been sought through a modular asynchronousloosely coupled framework, capable of augmenting additional observation sources to facilitatethe state estimation and tracking process. Besides GPS and IMU, the applied strategy makesuse of wheel speed sensor measurements, nonholonomic constraints and online estimationof IMU sensor biases as well wheel speed scalling factor. Theses augmentations have beenshown to increase the robustness of the localization module, under periods of GPS outage.The Extended Kalman Filter (EKF) has seen extensive usage for such sensor fusion tasks,however, the performance can be limited due to the propagation of the covariance throughlinearization of the underlying non-linear model. The Unscented Kalman Filter (UKF) avoidsthe issue of linearization based on jacobians. Instead, it uses a carefully chosen set ofsample points in order to accurately map the probability distribution. Correspondingly, thesurrounding literature also indicates towards the UKF out performing EKF in such tasks.Therefore, the present thesis also seeks to evaluate these claims.The EKF and SRUKF (Square Root UKF) instances of the developed algorithm have beentested on real sensor logs, recorded from a Scania test vehicle. Under no GPS outage situation,the implemented localization algorithm performs within a position RMSE of 60cm.The robustness of the localization algorithm, to GPS outages, is evaluated by simulating0-90% lengths of GPS unavailability, during the estimation process. Additionally, to unfoldthe impact of parameters, the individual modules within the suggested framework wereisolated and analysed with respect to their contribution towards the algorithm’s localizationperformance.Out of all, the online estimation of IMU sensor biases proved to be critical for increasingthe robustness of the suggested localization algorithm to GPS shortage, especially for the EKF.In terms of the distinction, both the EKF and the SRUKF performed to similar capabilities,however, the UKF showed better results for higher levels of GPS cuts. / Ett kritiskt krav för säker autonom körning är att ha en korrekt tillståndsuppskattning avfordonet. Ett av de mest förekommande men ändå tillförlitliga sätten för denna uppgift ärgenom integrationen av det inbyggda tröghetsnavigationssystemet (INS) och med Satellitnavi-gation (GNSS). Denna integration kan ytterligare underlättas genom sammanslagning avinformation från andra sensorer ombord. Utöver det upprätthåller ett markfordon sin egenuppsättning regler, genom icke-holonomiska begränsningar, som tillsammans med annanfordonsdynamik kan hjälpa till vid tillståndsuppskattningen.I detta projekt har en sekventiell probabilistisk slutledning följts, som sammansmälterde högfrekventa, kortsiktiga exakta INS-uppskattningarna, med lågfrekventa, driftfria GPS-observationer. Sammanslagningen av GPS och IMU har sökts genom ett modulärt asynkrontlöst kopplat ramverk, som kan utökas med ytterligare observationskällor för att underlättatillståndsuppskattningen och spårningsprocessen. Förutom GPS och IMU använder dentillämpade strategin mätningar av hjulhastighetssensorer, icke-holonomiska begränsningaroch onlineuppskattning av IMU-sensorbias samt hjulhastighetsskalningsfaktor. Dessa tillägghar visat sig öka robustheten hos lokaliseringsmodulen under perioder utan GPS-signal.Extended Kalman Filter (EKF) har sett omfattande användning för sådana sensorfusionsup-pgifter, men prestandan kan begränsas på grund av spridningen av kovariansen genomlinearisering av den underliggande icke-linjära modellen. Unscented Kalman Filter (UKF)undviker frågan om linearisering baserad på jacobianer. Istället använder den en noggrantutvald uppsättning provpunkter för att korrekt kartlägga sannolikhetsfördelningen. På motsva-rande sätt indikerar den omgivande litteraturen också mot UKF att utföra EKF i sådanauppgifter. Därför försöker denna avhandling också utvärdera dessa påståenden.EKF- och SRUKF-instanserna (Square Root UKF) av den utvecklade algoritmen hartestats på sensorloggar, inspelade från ett Scania-testfordon. Utan GPS-avbrott presterar denimplementerade lokaliseringsalgoritmen inom en position RMSE på 60 cm.Robustheten hos lokaliseringsalgoritmen, vid GPS-avbrott, utvärderas genom att simulera0-90% längder av GPS-otillgänglighet under uppskattningsprocessen. Utöver det har deenskilda modulerna inom det föreslagna ramverket isolerats och analyserats med avseendepå deras bidrag till algoritmens lokaliseringsprestanda.Av allt visade sig onlineuppskattningen av IMU-sensorbiaser vara avgörande för att ökarobustheten hos den föreslagna lokaliseringsalgoritmen mot GPS-brist, särskilt för EKF. Närdet gäller distinktionen presterade både EKF och SRUKF med liknande förmåga, men UKFvisade bättre resultat vid längre perioder utan GPS-signal.

<p>Unmanned aerial vehicles (UAVs) equipped with imaging systems and integrated global navigation satellite system/inertial navigation system (GNSS/INS) are used for a variety of applications. Disaster relief, infrastructure monitoring, precision agriculture, and ecological forestry growth monitoring are among some of the applications that utilize UAV imaging systems. For most applications, accurate 3D spatial information from the UAV imaging system is required. Deriving reliable 3D coordinates is conditioned on accurate geometric calibration. Geometric calibration entails both spatial and temporal calibration. Spatial calibration consists of obtaining accurate internal characteristics of the imaging sensor as well as estimating the mounting parameters between the imaging and the GNSS/INS units. Temporal calibration ensures that there is little to no time delay between the image timestamps and corresponding GNSS/INS position and orientation timestamps. Manual and automated spatial calibration have been successfully accomplished on a variety of platforms and sensors including UAVs equipped with frame and push-broom line cameras. However, manual and automated temporal calibration has not been demonstrated on both frame and line camera systems without the use of ground control points (GCPs). This research focuses on manual and automated spatial and temporal system calibration for UAVs equipped with GNSS/INS frame and line camera systems. For frame cameras, the research introduces two approaches (direct and indirect) to correct for time delay between GNSS/INS recorded event markers and actual time of image exposures. To ensure the best estimates of system parameters without the use of ground control points, an optimal flight configuration for system calibration while estimating time delay is rigorously derived. For line camera systems, this research presents the direct approach to estimate system calibration parameters including time delay during the bundle block adjustment. The optimal flight configuration is also rigorously derived for line camera systems and the bias impact analysis is concluded. This shows that the indirect approach is not a feasible solution for push-broom line cameras onboard UAVs due to the limited ability of line cameras to decouple system parameters and is confirmed with experimental results. Lastly, this research demonstrates that for frame and line camera systems, the direct approach can be fully-automated by incorporating structure from motion (SfM) based tie point features. Methods for feature detection and matching for frame and line camera systems are presented. This research also presents the necessary changes in the bundle adjustment with self-calibration to successfully incorporate a large amount of automatically-derived tie points. For frame cameras, the results show that the direct and indirect approach is capable of estimating and correcting this time delay. When a time delay exists and the direct or indirect approach is applied, horizontal accuracy of 1–3 times the ground sampling distance (GSD) can be achieved without the use of any ground control points (GCPs). For line camera systems, the direct results show that when a time delay exists and spatial and temporal calibration is performed, vertical and horizontal accuracy are approximately that of the ground sample distance (GSD) of the sensor. Furthermore, when a large artificial time delay is introduced for line camera systems, the direct approach still achieves accuracy less than the GSD of the system and performs 2.5-8 times better in the horizontal components and up to 18 times better in the vertical component than when temporal calibration is not performed. Lastly, the results show that automated tie points can be successfully extracted for frame and line camera systems and that those tie point features can be incorporated into a fully-automated bundle adjustment with self-calibration including time delay estimation. The results show that this fully-automated calibration accurately estimates system parameters and demonstrates absolute accuracy similar to that of manually-measured tie/checkpoints without the use of GCPs.</p>

In dieser Arbeit werden Postprocessing-Verfahren zum Steigern der Genauigkeit und Verfügbarkeit satellitengestützer Positionierungsverfahren, die ohne Inertialsensorik auskommen, untersucht. Ziel ist es, auch unter schwierigen Empfangsbedingungen, wie sie in urbanen Gebieten herrschen, eine Trajektorie zu erzeugen, deren Genauigkeit sie als Referenz für andere Verfahren qualifiziert. Zwei Ansätze werdenverfolgt: Die Verwendung von IGS-Daten sowie das Smoothing unter Einbeziehung von Sensoren aus der Fahrzeugodometrie. Es wird gezeigt, dass durch die Verwendung von IGS-Daten eine Verringerung des Fehlers um 50% bis 70% erreicht werden kann. Weiterhin demonstrierten die Smoothing-Verfahren, dass sie in der Lage sind, auch unter schlechten Empfangsbedingungen immer eine Genauigkeit im Dezimeterbereich zu erzielen.

<div>LiDAR units onboard airborne and terrestrial platforms have been established as a proven technology for the acquisition of dense point clouds for a wide range of applications, such as digital building model generation, transportation corridor monitoring, precision agriculture, and infrastructure monitoring. Furthermore, integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several high-level data processing activities such as reliable feature extraction and colorization of point clouds. However, the attainment of the full 3D point positioning potential of such systems is contingent on an accurate calibration of the mobile mapping unit as a whole. </div><div> </div><div> This research aims at proposing a calibration procedure for terrestrial multi-unit LiDAR systems to directly estimate the mounting parameters relating several spinning multi-beam laser scanners to the onboard GNSS/INS unit in order to derive point clouds with high positional accuracy. To ensure the accuracy of the estimated mounting parameters, an optimal configuration of target primitives and drive-runs is determined by analyzing the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud for different orientations of target primitives and different drive-run scenarios. This impact is also verified experimentally by simulating a bias in each mounting parameter separately. Next, the optimal configuration is used within an experimental setup to evaluate the performance of the proposed calibration procedure. Then, this proposed multi-unit LiDAR system calibration strategy is extended for multi-LiDAR multi-camera systems in order to allow a simultaneous estimation of the mounting parameters relating the different laser scanners as well as cameras to the onboard GNSS/INS unit. Such a calibration improves the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth. Finally, in order to qualitatively evaluate the calibration results for a generic mobile mapping system and allow the visualization of point clouds, imagery data, and their registration quality, an interface denoted as Image-LiDAR Interactive Visualization Environment (I-LIVE) is developed. Apart from its visualization functions (such as 3D point cloud manipulation and image display/navigation), I-LIVE mainly serves as a tool for the quality control of GNSS/INS-derived trajectory and LiDAR-camera system calibration. </div><div> </div><div> The proposed multi-sensor system calibration procedures are experimentally evaluated by calibrating several mobile mapping platforms with varying number of LiDAR units and cameras. For all cases, the system calibration is seen to attain accuracies better than the ones expected based on the specifications of the involved hardware components, i.e., the LiDAR units, cameras, and GNSS/INS units.</div>

Cette thèse porte sur l'étude des techniques d'estimation Bayesienne non-linéaire, et leur applications aux problèmes de synchronisation pour des systèmes de communication par satellite, ainsi qu'au calcul des bornes Bayesiennes pour le problème de synchronisation suréchantillonné. D'abord, on présente le filtrage de Kalman et les méthodes particulaires, et l'on propose une nouvelle vue d'ensemble des méthodes déterministes. Ensuite, on établie la modélisation pour le probléme de la synchronisation fractionnée dans des systémes satellite, et l'on calcule la borne de Cramér-Rao Bayesienne pour le probléme d'estimation de phase, et la borne de Cramér-Rao hybride pour le probléme d'estimation conjointe de phase et d'offset de fréquence. Dans un deuxième temps, on applique les méthodes de filtrage (Kalman, particulaires et déterministes) aux problèmes d'estimation de phase, d'estimation conjointe de phase et d'offset de fréquence, d'estimation de délai et d'estimation de phase avec des bruits non-Gaussiens. Les méthodes proposées ont montré de bonnes performances pour nos problèmes de synchronisation. On présente aussi dans cette thèse, trois études liées aux travaux principaux. Le premier concerne l'estimation conjointe des gains complexes et du délai dans un canal de Rayleigh à variations lentes pour des signaux CPM. Le deuxième présente l'utilisation des méthodes déterministes pour la localisation avec un réseau de capteurs. Et finalement, le troisième présente le couplage GNSS/INS ultra précis et une solution déterministes à cette problèmatique.