Unmanned aerial vehicles (UAV's) have recently gained popularity in military, civil service, agriculture, commercial, and hobby use. This is due in part to their affordability, which comes from advances in component technology. That technology includes microelectromechanical systems (MEMS) for inertial sensing, microprocessor technology for sequential algorithm processing, field programmable gate arrays (FPGA's) for parallel data processing, camera technology, global navigation satellite systems (GNSS's) for navigation, and battery technology such as the high energy density of lithium polymer batteries. Despite the success of the technology to date, there remains development before UAV's should be flying alongside manned aircraft or over populated areas. One concern is that UAV electronics are not as safe, reliable or robust as manned-aircraft electronics because UAV's are not certified by the FAA. Another concern for UAV operation is with control algorithms and sensors, particularly in the estimation of the aircraft state, which is the position, velocity, and orientation of the aircraft. Some problems, such as numerical stability of a control algorithm or flight in windy and turbulent conditions have only been solved for certain conditions of wind, weather, or maneuvers. Outside those conditions, the actual orientation of a flying craft can mislead to the control system, and the control system may not be able to recover without a crash. When pilots fly manned aircraft in instrument meteorological conditions, or conditions of limited visibility of the ground, terrain, and obstacles, the pilot must fly in a manner which avoids abrupt maneuvers which could disturb accuracy of the aircraft's instruments. In a UAV without a pilot, there is a need to estimate the position and orientation of a UAV in an absolute manner unambiguous relative to the Earth. The position and orientation estimate must not depend on carefully controlled flight paths, but instead the estimate must be robust in the presence of UAV flight dynamics. This thesis describes the design, implementation, and evaluation of a hardware platform for GPS based orientation sensing research. In this work, we considered a receiver with three or four RF sections, each connected to an antenna in a triangular or tetrahedral pyramid constellation. Specific requirements for the receiver hardware and functionality were created. Circuitry was designed to meet the requirements using commercial off-the-shelf (COTS) radio frequency (RF) modules, a mid-sized microcontroller, an FPGA, and other supporting components. A printed circuit board (PCB) was designed, fabricated, assembled, and tested. A GPS baseband processor was designed and coded in Verilog hardware description language. The design was synthesized and loaded to the FPGA, and the microcontroller was programmed to track satellites. With the hardware platform implemented, live satellite signals were found and tracked, and experiments were performed to explore the validity of GPS based orientation sensing using short antenna baselines. The platform successfully allows the user to develop correlator designs and explore carrier phase based orientation measurement using only software/Verilog modifications. Initial results of carrier phase based orientation sensing are promising, but the presence of multipath signal interference shows room for improvement to the baseband processing code.

No description available.

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.

A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

La navigation inertielle grâce aux capteurs intégrés dans les smartphones permet d’assurer une géolocalisation continue même en absence de signal GNSS. Ces capteurs bas coût délivrent néanmoins des mesures bruitées qui engendrent une dérive de la trajectoire. La technique PDR qui est une technique de navigation inertielle par détection de pas souffre de deux limites principales. La première est l’estimation de la longueur de pas car cette dernière dépend des caractéristiques physiques de chaque utilisateur, et la seconde est le résultat d’une dérive angulaire combinée avec un biais lié au portage du capteur à la main. Dans le contexte du projet HAPPYHAND, ce travail s’intéresse à l’exploitation de la carte pour corriger ces différentes erreurs. Un réseau de navigation topologique est exploité pour corriger à la fois les erreurs angulaires et calibrer le modèle de longueur de pas. Ce modèle est ensuite augmenté par un processus de mise à jour de position par détection de points d’intérêt. / Smartphone navigation using the low-cost embedded sensors in off the shelf smartphones can provide a continuous solution in GNSS-denied environments. The most widely adopted approach is Pedestrian Dead Reckoning (PDR) that uses acceleration and angular velocity to estimate the user’s position. Yet, consumer grade sensors deliver noisy measurements that may result into a drift in the estimated trajectory. One major challenge is to estimate accurately step length information since it depends on physiological features that are specific to each user. In addition, angular biases are more likely to be introduced in the orientation estimation process with handheld devices. This is mainly due to the high degree of freedom of hand motion. In the context of a national project called HAPPYHAND, the main goal of this work is to exploit map information as far as possible in order to mitigate the previous inherent limitations to the PDR approach. First, a topological network extracted from the map is proposed in order to correct the angular errors and calibrate the step length model. Second, context awareness is adopted in order to provide regular and frequent position updates thanks to a point of interest online detection scheme.

The aim of this thesis is to write a multisensor navigation algorithm and to design a test setup. After doing these, test the algorithm by using the test setup. In navigation, dead reckoning is a procedure to calculate the position from initial position with some measured inputs. These measurements do not include absolute position data. Using only an inertial measurement unit is an example for dead reckoning navigation. Calculating position and velocity with the inertial measurement unit is highly erroneous because, this calculation requires integration of acceleration data. Integration means accumulation of errors as time goes. For example, a constant acceleration error of 0.1 m/s^2 on 1 m/s^2 of acceleration will lead to 10% of position error in only 5 seconds. In addition to this, wrong calculation of attitude is going to blow the accumulated position errors. However, solving the navigation equations while knowing the initial position and the IMU readings is possible, the IMU is not used solely in practice. In literature, there are studies about this topic and in these studies / some other sensors aid the navigation calculations. The aiding or fusion of sensors is accomplished via Kalman filter. In this thesis, a navigation algorithm and a sensor fusion algorithm were written. The sensor fusion algorithm is based on estimation of IMU errors by use of a Kalman filter. The design of Kalman filter is possible after deriving the mathematical model of error propagation of mechanization equations. For the sensor fusion, an IMU, two incremental encoders and a digital compass were utilized. The digital compass outputs the orientation data directly (without integration). In order to find the position, encoder data is calculated in dead reckoning sense. The sensor triplet aids the IMU which calculates position data by integrations. In order to mount these four sensors, an unmanned tracked vehicle prototype was manufactured. For data acquisition, an xPC&ndash / Target system was set. After planning the test procedure, the tests were performed. In the tests, different paths for different sensor fusion algorithms were experimented. The results were recorded in a computer and a number of figures were plotted in order to analyze the results. The results illustrate the benefit of sensor fusion and how much feedback sensor fusion is better than feed forward sensor fusion.

Inertial navigation systems (INSs) have the well-known advantages of being self-contained, weatherproof, jam-proof, and non-self-revealing while providing stable navigation information with little high-frequency noise. However, their single most important drawback is the growth of their error cumulatively with time in an unbounded manner. Navigation systems based on position fixing, in contrast, offer bounded errors in the long term, but their output is usually contaminated with strong high-frequency noise. To harness the advantages of both types of systems, INSs have been traditionally aided or augmented by one or more fixing system(s). Such an arrangement preserves the excellent short-term stability and damping (i.e. high-frequency rejection) capability of INSs while limiting its long-term drift. In recent years, the availability of navigation information from the Global Positioning System (GPS) reliably and accurately over the entire globe has made it a natural choice as the means of augmentation of INSs. An integrated navigation system combining data from two or more ‘pure’systems is called a hybrid navigation system (HNS). There is no unique way of combining navigation information from the INS and GPS. Depending on the goals and specifications of the overall navigation system, the instrument and equipment available, cost constraints, and technology options, the scheme for integrating INS and GPS may take one of many forms. In generic terms integration of diverse ‘pure’ navigation systems can be performed at various levels. At the simplest and most basic level, each system may be allowed to run independently, generating its own navigation data separately which may then be combined periodically to reset any accumulated error. At the other extreme, the two (or more) systems may be intimately coupled right at their raw data levels in a quasi-continuous manner with the intention of maximising their mutually beneficial effect and deriving the ‘best’ possible navigation information. Hybrid navigation architectures have been a subject of much research and development, and a significant body of information is available on the subject. However, there are clear gaps in open literature on many practical issues that arise in the context of implementing specific HNSs. In this thesis we investigate the architecture, implementation and performance issues of HNSs that combine stand-alone INS and GPS units. The thesis consists of eight chapters. The first chapter offers an introduction to the navigation problem and discusses the basic types of navigation including inertial and satellite navigation. Inertial sensors such as gyroscopes and accelerometers and the GPS are discussed in some detail. The types and principle of gyroscopes and accelerometers and the error sources in inertial navigation are briefly covered, as also the advantages and disadvantages of INS and the trends in inertial system development.The chapter also touches upon GPS segments (space, control and user), the theory and determination of position fix, and the GPS error sources. Mention is also made of the types of GPS receiver available and the trends in GPS technologies. Integration of INS and GPS and its benefits are discussed and a set of specifications for an integrated system is laid out to serve as the basis for the configurations proposed later. The second chapter, in its three sections, provides a summary of the significant literature relevant to INS and GPS in the context of their integration. Chapter three discusses mechanisation aspects of the INS-GPS hybrid navigation system. This chapter is divided into three sections. In the first section the frames of reference, INS mechanisation and the error equations are discussed. The definitions for the various frames such as body, platform, local level, geodetic, Earth-centred-Earth-fixed (ECEF), and the computer frame are mentioned along with the direction cosine matrices for the transformation of frames. In the second section various types of mechanisation of INS and the summary of tilt, velocity and position equations are described. The INS can be mechanised in two ways: the stable platform and the strap-down. In this chapter the general error equations for platform tilt, velocity and position are listed. Platform-based systems can be mechanized as one of the following types, viz. unipolar, Focualt, north pointing and wander azimuth. The definitions and summary of the tilt, velocity and position, and the error equations are given for all these types of mechanization. The accelerometer and gyro error models are discussed. It is pointed out that inclusion of all the possible INS states in the model would lead to a 45-state system which would be too complex to handle on board. The scope for reduction of model order and the effect of such reduction are brought out. The section ends with a summary of the INS error equations considered for implementation. In the third section the GPS principle and derivation of navigation solutions based on GPS measurements are dealt with. GPS error modelling, computation of DOP (dilution of precision), and clock modelling are also discussed. In this section the navigation solution for various classes of users – stationary, low-dynamics, medium-dynamics and high-dynamics – are discussed. The INS model and the clock model defined in this chapter are used in configuring the integrated system model later. Chapter four discusses the various HNS configurations and their implementation to mitigate the INS error. Three levels of integration are considered: a. Output coupled: The INS needs initial alignment during which the INS position and velocity are initialised with the precisely known co-ordinates and components at the starting location. Starting with these initial conditions, the INS-sensed accelerations are continuously integrated to yield the current velocity and position. As mentioned earlier, the INS error is dependent on this initial value and further increases with time. If the initial position and velocity inputs are precise, the short-term INS accuracy (typically for the first 10-15 minutes in case of aircraft) is usually within acceptable limits. Further error built up during longer flights can be reduced by periodic updation of INS with the precise position and/or velocity values. To achieve this the pilot may, for example, fly over waypoints whose co-ordinates are precisely known. This would constitute a physical or manual method of INS re-initialisation. A better and more modern method is to use precise GPS-derived information to reinitialise the INS periodically and automatically. b. Medium coupled: Another way of mitigating the INS error build-up is by using medium-coupled HNS wherein the INS errors are estimated using the GPS measurements as reference. The INS outputs are corrected by applying these error estimates. The important point to note here is that in medium coupling, the errors in the INS states are considered instead of the states themselves. The final geodetic outputs from the two systems are used as measurements. A twelve-state indirect feed-forward Kalman filter is used to estimate the INS position error. c. Tightly coupled: The basic measurements from the GPS are pseudoranges which are the distances from the user to the GPS satellites. By making a minimum of four such measurements the GPS receiver computes the user location in the geodetic coordinates. Conversely, knowing the user position from INS, it is possible to calculate the expected pseudoranges to known GPS satellite locations. Comparing the measured and the computed pseudoranges, the filter estimates the errors in the INS position. In this work a seventeen-state, feed-forward, indirect Kalman filter is configured to estimate the INS-derived pseudorange errors. These errors are then translated into positional errors which are used to correct the indicated INS positions. In configuring the filter it is assumed that the INS and GPS are physically separated and data transfer is through the interface buses. In this chapter the simulators used for validation and performance estimation of the configurations are also described. Two simulators are used to validate the hybrid system, namely, software-and hardware-based simulators. The simulators simulate the six-degree-of-freedom of trajectory generator, and error models of INS and GPS. The truth data from the trajectory generator are combined with the INS error and GPS error to get the INS and GPS outputs respectively. The fifth and sixth chapters covers the validation of the above-mentioned three configurations. Since analysis of output coupled systems is rather straightforward, simulation and validation of the configuration are carried out for the medium and tightly coupled systems Covariance analysis and Error analysis modes of simulation are carried out to study and validate the behaviour of the configurations. In covariance analysis one obtains the root mean square (rms) value of the errors obtained from several Monte Carlo runs. It gives an estimate of the lower bound of the system errors. Covariance simulation provides a degree of confidence in the error model but is generally not sufficient to expose the complete behaviour of the system. For detailed investigation, error simulation needs to be carried out for the entire navigation system. In the thesis, covariance simulation is carried out for both the configurations to check the sensitivity of the system to measurement update rates, process noise, update times for the transition matrix, and also for the validity of the truncation of the Taylor series expansion. The details of the simulation processes and their results are discussed in these chapter. The seventh chapter makes a performance comparison of the configurations and draws inferences for practical hybrid system implementation. From the comparisons it is seen that the loosely coupled configuration is the simplest. In this configuration there is no requirement of the Kalman filter. The accuracy depends on the update rate. If the position update is made, for example, once every 600 s then the error in the combined system will be limited to the sum of the error due to the GPS and that accumulated in the INS alone over the of 600 s interval. There is no coordinate transformation required. In the case of medium coupled filter the addition of process noise to the GPS clock model is not critical. The position accuracy achieved is around 2 m (rms). The coordinate transformations are only from the body to platform, and platform to geodetic axes. The observation matrix is very simple in this case and the computation burden is low. Dynamic tuning of the measurement matrix is not required in real time.In the case of tightly coupled configuration the addition of a certain amount of process noise deliberately to the GPS clock model is critical. The position accuracy achieved with tight coupling is around found to be 34 m (rms) without the addition of process noise. On addition of a controlled amount of noise to the GPS clock bias and clock drift states and inclusion of measurement noise as a function of GPS signal strength, the position accuracy improves significantly, to about 7m (rms). Figures 2a and 2b below depict the behaviour before and after inclusion of the noise. The coordinate transformations are from body to platform, platform to geodetic, and geodetic to ECEF coordinates, and vice versa. The observation matrix (H) for this integration model is very complicated, and the computational burden is very high. In this configuration H transfers the measurements from metres to radians. Dynamic tuning of measurement matrix is required in this case. Chapter eight of the thesis summarises the results and lists out the conclusions arrived at from the study. It also includes a section with suggestions for future work in this direction.

Autonomous land vehicles (ALVs) have received considerable attention after their introduction into military and commercial applications. ALVs still stand as a challenging research topic. One of the main problems arising in ALV operations is the navigation accuracy while the other is the dynamic effects of road irregularities which may prevent the vehicle and its cargo to function properly. In this thesis, we propose intelligent solutions to these two basic problems of ALV. First, an intelligent method is proposed to enhance the performance of a coupled global positioning/inertial navigation system (GPS/INS) for land navigation applications during the GPS signal loss. Our method is based on using an artificial neural network (ANN) to intelligently aid the GPS/INS coupled navigation system in the absence of GPS signals. The proposed enhanced GPS/INS is used in the dynamic environment of a tour of an autonomous van and we provide the results here. GPS/INS+ANN system performance is thus demonstrated with the land trials. Secondly, our work focuses on the identification and enlargement of the stability region of the ALV. In this thesis, the domain of attraction of the ALV is found to be patched by chaotic and regular regions with chaotic boundaries which are extracted using novel technique of cell mapping equipped with measures of fractal dimension and rough sets. All image cells in the cellular state space, with their individual fractal dimension are classified as being members of lower approximation (surely stable), upper approximation (possibly stable) or boundary region using rough set theory. The obtained rough set with fractal dimension as its attribute is used to model the uncertainty of the regular regions. This uncertainty is then smoothed by a reinforcement learning algorithm in order to enlarge regular regions that are used for chassis control, critical in ALV in preventing vibration damages that can harm the payload. Hence, we will make ALV work in the largest safe area in dynamical sense and prevent the vehicle and its cargo.

L'estimation précise du mouvement 3D d'une caméra relativement à une scène rigideest essentielle pour tous les systèmes de navigation visuels. Aujourd'hui différentstypes de capteurs sont adoptés pour se localiser et naviguer dans des environnementsinconnus : GPS, capteurs de distance, caméras, capteurs magnétiques, centralesinertielles (IMU, Inertial Measurement Unit). Afin d'avoir une estimation robuste, lesmesures de plusieurs capteurs sont fusionnées. Même si le progrès technologiquepermet d'avoir des capteurs de plus en plus précis, et si la communauté de robotiquemobile développe algorithmes de navigation de plus en plus performantes, il y aencore des défis ouverts. De plus, l'intérêt croissant des la communauté de robotiquepour les micro robots et essaim de robots pousse vers l'emploi de capteurs à bas poids,bas coût et vers l'étude d'algorithmes à faible complexité. Dans ce contexte, capteursinertiels et caméras monoculaires, grâce à leurs caractéristiques complémentaires,faible poids, bas coût et utilisation généralisée, représentent une combinaison decapteur intéressante.Cette thèse présente une contribution dans le cadre de la navigation inertielle assistéepar vision et aborde les problèmes de fusion de données et estimation de la pose, envisant des algorithmes à faible complexité appliqués à des micro-véhicules aériens.Pour ce qui concerne l'association de données, une nouvelle méthode pour estimer lemouvement relatif entre deux vues de caméra consécutifs est proposée.Celle-ci ne nécessite l'observation que d'un seul point caractéristique de la scène et laconnaissance des vitesses angulaires fournies par la centrale inertielle, sousl'hypothèse que le mouvement de la caméra appartient localement à un planperpendiculaire à la direction de la gravité. Deux algorithmes très efficaces pouréliminer les fausses associations de données (outliers) sont proposés sur la base decette hypothèse de mouvement.Afin de généraliser l'approche pour des mouvements à six degrés de liberté, deuxpoints caracteristiques et les données gyroscopiques correspondantes sont nécessaires.Dans ce cas, deux algorithmes sont proposés pour éliminer les outliers.Nous montrons que dans le cas d'une caméra monoculaire montée sur un quadrotor,les informations de mouvement fournies par l'IMU peuvent être utilisées pouréliminer de mauvaises estimations.Pour ce qui concerne le problème d'estimation de la pose, cette thèse fournit unesolution analytique pour exprimer la pose du système à partir de l'observation de troispoints caractéristiques naturels dans une seule image, une fois que le roulis et letangage sont obtenus par les données inertielles sous l'hypothèse de terrain plan.Afin d'aborder le problème d'estimation de la pose dans des environnements sombresou manquant de points caractéristiques, un système équipé d'une caméra monoculaire,d'une centrale inertielle et d'un pointeur laser est considéré. Grace à une analysed'observabilité il est démontré que les grandeurs physiques qui peuvent êtredéterminées par l'exploitation des mesures fourni par ce systeme de capteurs pendantun court intervalle de temps sont : la distance entre le système et la surface plane ;la composante de la vitesse du système qui est orthogonale à la surface ; l'orientationrelative du système par rapport à la surface et l'orientation de la surface par rapport àla gravité. Une méthode récursive simple a été proposée pour l'estimation de toutesces quantités observables.Toutes les contributions de cette thèse sont validées par des expérimentations à l'aidedes données réelles et simulées. Grace à leur faible complexité de calcul, lesalgorithmes proposés sont très appropriés pour la mise en oeuvre en temps réel surdes systèmes ayant des ressources de calcul limitées. La suite de capteur considéréeest monté sur un quadrotor, mais les contributions de cette thèse peuvent êtreappliquées à n'importe quel appareil mobile. / Accurate egomotion estimation is of utmost importance for any navigation system.Nowadays di_erent sensors are adopted to localize and navigate in unknownenvironments such as GPS, range sensors, cameras, magnetic field sensors, inertialsensors (IMU). In order to have a robust egomotion estimation, the information ofmultiple sensors is fused. Although the improvements of technology in providingmore accurate sensors, and the efforts of the mobile robotics community in thedevelopment of more performant navigation algorithms, there are still openchallenges. Furthermore, the growing interest of the robotics community in microrobots and swarm of robots pushes towards the employment of low weight, low costsensors and low computational complexity algorithms. In this context inertial sensorsand monocular cameras, thanks to their complementary characteristics, low weight,low cost and widespread use, represent an interesting sensor suite.This dissertation represents a contribution in the framework of vision-aided inertialnavigation and tackles the problems of data association and pose estimation aimingfor low computational complexity algorithms applied to MAVs.For what concerns the data association, a novel method to estimate the relative motionbetween two consecutive camera views is proposed. It only requires the observationof a single feature in the scene and the knowledge of the angular rates from an IMU,under the assumption that the local camera motion lies in a plane perpendicular to thegravity vector. Two very efficient algorithms to remove the outliers of the featurematchingprocess are provided under the abovementioned motion assumption. Inorder to generalize the approach to a 6DoF motion, two feature correspondences andgyroscopic data from IMU measurements are necessary. In this case, two algorithmsare provided to remove wrong data associations in the feature-matching process. Inthe case of a monocular camera mounted on a quadrotor vehicle, motion priors fromIMU are used to discard wrong estimations.For what concerns the pose estimation problem, this thesis provides a closed formsolution which gives the system pose from three natural features observed in a singlecamera image, once the roll and the pitch angles are obtained by the inertialmeasurements under the planar ground assumption.In order to tackle the pose estimation problem in dark or featureless environments, asystem equipped with a monocular camera, inertial sensors and a laser pointer isconsidered. The system moves in the surrounding of a planar surface and the laserpointer produces a laser spot on the abovementioned surface. The laser spot isobserved by the monocular camera and represents the only point feature considered.Through an observability analysis it is demonstrated that the physical quantities whichcan be determined by exploiting the measurements provided by the aforementionedsensor suite during a short time interval are: the distance of the system from the planarsurface; the component of the system speed that is orthogonal to the planar surface;the relative orientation of the system with respect to the planar surface; the orientationof the planar surface with respect to the gravity. A simple recursive method toperform the estimation of all the aforementioned observable quantities is provided.All the contributions of this thesis are validated through experimental results usingboth simulated and real data. Thanks to their low computational complexity, theproposed algorithms are very suitable for real time implementation on systems withlimited on-board computation resources. The considered sensor suite is mounted on aquadrotor vehicle but the contributions of this dissertations can be applied to anymobile device.

CAPES / Um sistema de navegação inercial (INS) é um dispositivo autônomo capaz de determinar sua própria posição a partir de medições fornecidas por sensores inerciais. Para a presente dissertação, uma ferramenta para a simulação e validação de sistemas de navegação inercial foi desenvolvida. Essa ferramenta permite que as soluções de navegação de um INS simulado possam ser comparadas a soluções de referência analiticamente exatas. A partir dos resultados dessa comparação, o usuário pode decidir pela validade ou não validade dos algoritmos de navegação do INS simulado. A ferramenta foi desenvolvida com um foco essencialmente didático para prover ao usuário um meio para a melhor compreensão do funcionamento dos complexos algoritmos associados à navegação inercial. Apesar do foco didático, a ferramenta também possui um caráter prático relevante já que ela efetivamente permite a validação de diferentes configurações de algoritmos consistentes com o estado da arte da navegação inercial. A presente dissertação, portanto, apresenta a ferramenta desenvolvida e demonstra seu correto funcionamento através de um conjunto relevante de experimentos de simulação. / An inertial navigation system (INS) is an autonomous device that determines its own position based on measurements provided by inertial sensors. For this dissertation, a simulation and validation tool for inertial navigation systems has been developed. This tool allows the navigation solutions generated by a simulated INS to be compared against analytically exact reference solutions. Based on the results of this comparison, the user can decide if the simulated INS navigation algorithms are valid or not valid. The tool has been developed with an essentially didactic focus in order to provide the user with a way to better understand how the complex inertial navigation algorithms work. Despite the didactic focus, the developed tool has also a relevant practical aspect since it effectively permits the validation of different configurations of algorithms that are consistent with the inertial navigation state of the art. This dissertation, therefore, describes the developed tool and demonstrates its correct behavior through a relevant set of simulation experiments.