This thesis applies the methods of previous work in aerial magnetic field mapping and use in state estimation to the Virginia Tech Swing Space motion capture indoor facility. State estimation with magnetic field data acquired from a quadrotor is comparatively performed with Gaussian process regression, a multiplicative extended Kalman filter, and a particle filter to estimate the position and attitude of an uncrewed aircraft system (UAS) at any point in the motion capture testing environment. Motion capture truth data is used in the analysis. The first experimental method utilized in this thesis is Gaussian process regression. This machine learning tool allows us to create three-dimensional magnetic field maps of the indoor test space by collecting magnetic field vector data with a small UAS. Here, the maps illustrate the 3D magnetic field strengths and directions in the Virginia Tech Swing Space motion capture lab. Also, the magnetic field spatial variation of the test space is analyzed, yielding higher magnetic field gradient at lower heights above the ground. Next, the multiplicative extended Kalman filter is used with our Gaussian process regression magnetic field maps to estimate the attitude of the quadrotor. The results indicate an increase in attitude estimation accuracy when magnetic field mapping is utilized compared to when it is not. Here, results show that the addition of aerial magnetic field mapping leads to enhanced attitude estimation. Finally, the particle filter is utilized with support from our magnetic field maps to estimate the position of a small quadrotor UAS. The magnetic field maps allow us to obtain UAS position vectors by tracking UAS movement through magnetic field data. The particle filter gives three-dimensional position estimates to within 0.2 meters for five out of our eight test flights. The root mean square error is within 0.1 meters for each test flight. The effects of magnetic field spatial variation are also analyzed. The accuracy of position estimation is higher for two out the four flights in the maximum magnetic gradient area, while the accuracy is similar in both minimum and maximum gradient regions for the remaining two flights. There is evidence to support an increase in accuracy for high magnetic variation areas, but further work is needed to confirm utility for practical applications. / Master of Science / This thesis investigates airborne magnetic field mapping for the Virginia Tech Swing Space motion capture indoor facility. Position and attitude estimation with magnetic field data acquired from a small uncrewed aircraft system (UAS) is comparatively performed with multiple estimation methods. Motion capture truth data is used in analyses. The first data processing method is called Gaussian process regression. This machine learning tool allows us to create magnetic field maps of the indoor test space by averaging or regressing field estimates over collected UAS data. The maps illustrate the magnetic field strengths and directions over a three dimensional volume in the Virginia Tech Swing Space motion capture lab. Next, a multiplicative extended Kalman filter is used with our Gaussian process regression magnetic field maps to estimate UAS attitude. Results show improvement in attitude estimation accuracy when magnetic field mapping is utilized compared to when it is not. Finally, a particle filter method is utilized with our magnetic field maps to estimate UAS position. The particle filter estimates three-dimensional UAS position estimates to within 0.2 meters for five out of our eight test flights. The effects of magnetic field spatial variation are also analyzed, indicating the need for future work before magnetic field based position estimation can be practically applied.

In order for a robot to operate autonomously in an environment, it must be able to locate itself within it. A robot's position and orientation cannot be directly measured by physical sensors, so estimating it is a non-trivial problem. Some sensors provide this information, such as the Global Navigation Satellite System (GNSS) and Motion capture (Mo-cap). Nevertheless, these sensors are expensive to set up, or they are not useful in environments where autonomous vehicles are often deployed. Our proposal explores a new approach to sensing for relative motion and position estimation. It consists of one vision sensor and a marker that utilizes moiré phenomenon to estimate the position of the vision sensor by using Convolutional Neural Networks (CNN) trained to estimate the position from the pattern shown on the marker. We share the process of data collection and training of the network and share the hyperparameter search method used to optimize the structure of the network. We test the trained network in a setup to evaluate its ability in estimating position. The system achieved an average absolute error of 1 cm, showcasing a method that could be used to overcome the current limitations of vision approaches in pose estimation.

In this thesis, we study certain aspects of signal source/target localization by sensory agents and their biomedical applications. We first focus on a generic distance measurement based problem: Estimation of the location of a signal source by a sensory agent equiped with a distance measurement unit or a team of such a sensory agent. This problem was addressed in some recent studies using a gradient based adaptive algorithm. In this study, we design a least-squares based adaptive algorithm with forgetting factor for the same task. Besides its mathematical background, we perform some simulations for both stationary and drifting target cases. The least-squares based algorithm we propose bears the same asymptotic stability and convergence properties as the gradient algorithm previously studied. It is further demonstrated via simulation studies that the proposed least-squares algorithm converges significantly faster to the resultant location estimates than the gradient algorithm for high values of the forgetting factor, and significantly reduces the noise effects for small values of the forgetting factor. We also focus on the problem of localizing a medical device/implant in human body by a mobile sensor unit (MSU) using distance measurements. As the particular distance measurement method, time of flight (TOF) based approach involving ultra wide-band signals is used, noting the important effects of the medium characteristics on this measurement method. Since human body consists of different organs and tissues, each with a different signal permittivity coefficient and hence a different signal propagation speed, one cannot assume a constant signal propagation speed environment for the aforementioned medical localization problem. Furthermore, the propagation speed is unknown. Considering all the above factors and utilizing a TOF based distance measurement mechanism, we use the proposed adaptive least-square algorithm to estimate the 3-D location of a medical device/implant in the human body. In the design of the adaptive algorithm, we first derive a linear parametric model with the unknown 3-D coordinates of the device/implant and the current signal propagation speed of the medium as its parameters. Then, based on this parametric model, we design the proposed adaptive algorithm, which uses the measured 3-D position of the MSU and the measured TOF as regressor signals. After providing a formal analysis of convergence properties of the proposed localization algorithm, we implement numerical tests to analyze the properties of the localization algorithm, considering two types of scenarios: (1) A priori information regarding the region, e.g quadrant (among upper-left, upper-right, lower-left, lower-right of the human body), of the implant location is available and (2) such a priori information is not available. In (1), assuming knowledge of fixed average relative permittivity for each region, we established that the proposed algorithm converges to an estimate with zero estimation error. Moreover, different white Gaussian noises are added to emulate the TOF measurement disturbances, and it is observed that the proposed algorithm is robust to such noises/disturbances. In (2), although perfect estimation is not achieved, the estimation error is at a low admissible level. In addition, for both cases (1) and (2), forgetting factor effects have been investigated and results show that use of small forgetting factor values reduces noise effects significantly, while use of high forgetting factor values speeds up convergence of the estimation.

Inertial sensors such as Gyroscope and Accelerometer show systematic as well as random errors in the measurement. Furthermore, double integration method shows accumulation of error in position estimation due to inherent accelerometer bias drift. The primary objective of this research was to evaluate ADXL 335 acceleration sensor for better position estimation using acceleration bias drift error model. In addition, measurement data was recorded with four point rotation test for investigation of error characteristics. The fitted model was validated by using nonlinear regression analysis. The secondary objective was to examine the effect of bias drift and scale factor errors by introducing error model in Kalman Filter smoothing algorithm. The study showed that the accelerometer may be used for short distance mobile robot position estimation. This research would also help to establish a generalized test procedure for evaluation of accelerometer in terms of sensitivity, accuracy and data reliability.

In this thesis, we study certain aspects of signal source/target localization by sensory agents and their biomedical applications. We first focus on a generic distance measurement based problem: Estimation of the location of a signal source by a sensory agent equiped with a distance measurement unit or a team of such a sensory agent. This problem was addressed in some recent studies using a gradient based adaptive algorithm. In this study, we design a least-squares based adaptive algorithm with forgetting factor for the same task. Besides its mathematical background, we perform some simulations for both stationary and drifting target cases. The least-squares based algorithm we propose bears the same asymptotic stability and convergence properties as the gradient algorithm previously studied. It is further demonstrated via simulation studies that the proposed least-squares algorithm converges significantly faster to the resultant location estimates than the gradient algorithm for high values of the forgetting factor, and significantly reduces the noise effects for small values of the forgetting factor. We also focus on the problem of localizing a medical device/implant in human body by a mobile sensor unit (MSU) using distance measurements. As the particular distance measurement method, time of flight (TOF) based approach involving ultra wide-band signals is used, noting the important effects of the medium characteristics on this measurement method. Since human body consists of different organs and tissues, each with a different signal permittivity coefficient and hence a different signal propagation speed, one cannot assume a constant signal propagation speed environment for the aforementioned medical localization problem. Furthermore, the propagation speed is unknown. Considering all the above factors and utilizing a TOF based distance measurement mechanism, we use the proposed adaptive least-square algorithm to estimate the 3-D location of a medical device/implant in the human body. In the design of the adaptive algorithm, we first derive a linear parametric model with the unknown 3-D coordinates of the device/implant and the current signal propagation speed of the medium as its parameters. Then, based on this parametric model, we design the proposed adaptive algorithm, which uses the measured 3-D position of the MSU and the measured TOF as regressor signals. After providing a formal analysis of convergence properties of the proposed localization algorithm, we implement numerical tests to analyze the properties of the localization algorithm, considering two types of scenarios: (1) A priori information regarding the region, e.g quadrant (among upper-left, upper-right, lower-left, lower-right of the human body), of the implant location is available and (2) such a priori information is not available. In (1), assuming knowledge of fixed average relative permittivity for each region, we established that the proposed algorithm converges to an estimate with zero estimation error. Moreover, different white Gaussian noises are added to emulate the TOF measurement disturbances, and it is observed that the proposed algorithm is robust to such noises/disturbances. In (2), although perfect estimation is not achieved, the estimation error is at a low admissible level. In addition, for both cases (1) and (2), forgetting factor effects have been investigated and results show that use of small forgetting factor values reduces noise effects significantly, while use of high forgetting factor values speeds up convergence of the estimation.

The purpose of this thesis is to investigate the possibility of using aerial or satellite images or eventually digital elevation models in order to localize the UAV helicopter in the environment. Matching techniques are investigated in order to match the available on-board image of the area with the live images acquired by the on-board video camera. The problem is interesting because it can provide a redundancy for the UAV navigation system which is based only on GPS. The thesis is in the context of the development of an integrated system for navigation using image sequences from an aircraft. The system is composed of relative position estimation, which computes the current position of the helicopter by accumulating relative displacement extracted from successive aerial images. These successive aerial images are then matched using certain image matching techniques.

Electromagnetically driven dosing pumps are suitable for metering any kind of liquid in motor vehicles in a precise manner. Due to the working principle and the pump design, an undesired noise occurs when the armature reaches the mechanical end stops. The noise can be reduced by an adequate self-learning control of the supply energy using a position estimation and velocity control. Based on preliminary investigations /1/, a method for noise reduction is realised by using a user-friendly, tiny and cost-efficient hardware, which enables a use in series manufacturing. The method requires only a voltage and current measurement as input signals. The core of the hardware is an 8-bit microcontroller with 8 kilobytes flash memory including necessary peripherals. A smart software development enables an implementation of the entire noise reduction method onto the tiny flash memory.

This work compares localization techniques used in mobile robotics. Localization - how to determine one's own position within a space - is one of the fundamental challenges of robotics. The introduction is devoted to a detailed description of localization and to the categorization of localization techniques. In subsequent chapters, category by category, various localization techniques and their variants are described and their strengths and weaknesses are compared. The work successively addresses: probabilistic localization techniques used for inaccurate sensor measurements processing and for providing reliable position estimate; relative localization techniques used for evaluation of relative changes in the robot position; and absolute localization techniques for finding and estimating the absolute position of the robot in the environment.

A estimação de posição é o resultado direto da reconstrução de cenas, um dos ramos da visão computacional. É também uma informação importante para o controle de sistemas mecatrônicos, e em especial para os sistemas robóticos autônomos. Como uma aplicação de engenharia, o desempenho de tal sistema deve ser avaliado em termos de eficiência e eficácia, medidas traduzidas respectivamente pelo custo de processamento e pela quantificação do erro. A geometria epipolar é um campo da visão computacional que fornece formalismo matemático e técnicas de reconstrução de cenas a partir de uma par de imagens, através de pontos correspondentes entre elas. Através deste formalismo é possível determinar a incerteza dos métodos de estimação de posição, que são relativamente simples e podem atingir boa precisão. Dentre os sistemas robóticos autônomos destacam-se os ROVs - do inglês \"Remotely Operated Vehicles\" - ou veículos operados remotamente, muito utilizados em tarefas submarinas, e cuja necessidade crescente de autonomia motiva o desenvolvimento de um sensor de visão com características de baixo consumo de energia, flexibilidade e inteligência. Este sensor pode consistir de uma câmera CCD e algoritmos de reconstrução de cena baseados em geometria epipolar entre imagens. Este estudo visa fornecer um comparativo de resultados práticos da estimação de posição através da geometria epipolar entre imagens, como parte da implementação de um sensor de visão para robôs autônomos. Os conceitos teóricos abordados são: geometria projetiva, modelo de câmera, geometria epipolar, matriz fundamental, reconstrução projetiva, re-construção métrica, algoritmos de determinação da matriz fundamental, algoritmos de reconstrução métrica, incerteza da matriz fundamental e complexidade computacional. Os resultados práticos baseiam-se em simulações através de imagens geradas por computador e em montagens experimentais feitas em laboratório que simulam situações práticas. O processo de estimação de posição foi realizado através da implementação em MATLAB® 6.5 dos algoritmos apresentados na parte teórica, e os resultados comparados e analisados quanto ao erro e complexidade de execução. Dentre as principais conclusões é apresentado a melhor escolha para a implementação de sensor de visão de propósito geral - o Algoritmo de 8 Pontos Correspondentes Normalizado. São apresentadas também as condições de utilização de cada método e os cuidados necessários na interpretação dos resultados. / Position estimation is the direct result of scene reconstruction, one of computer vision\'s fields. It is also an important information for the control of mechanical systems - specially the autonomous robotic systems. As an engineering application, those systems\' performance must be evaluated in terms of efficiency and effectiveness, measured by processing costs and error quantification. The epipolar geometry is a field of computer vision that supply mathematical formalism and scene reconstruction techniques that are based on the correspondences between two images. Through this formalism it is possible to stipulate the uncertainty of the position estimation methods that are relatively simple and can give good accuracy. Among the autonomous robotic systems, the ROVs - Remotely Operated Vehicles - are of special interest, mostly employed in submarine activities, and whose crescent autonomy demand motivates the development of a vision sensor of low power consumption, flexibility and intelligence. This sensor may be constructed with a CCD camera and the scene reconstruction algorithms based on epipolar geometry. This work aims to build a comparison of practical results of position estimation through epipolar geometry, as part of a vision sensor implementation for autonomous robots. The theory presented in this work comprises of: projective geometry, camera model, epipolar geometry, fundamental matrix, projective reconstruction, metric reconstruction, fundamental matrix algorithms, metric reconstruction algorithms, fundamental matrix uncertainty, and computational complexity. The practical results are based on computer generated simulations and experimental assemblies that emulate practical issues. The position estimation was carried out by MATLAB® 6.5 implementations of the algorithms analyzed in the theoretical part, and the results are compared and analyzed in respect of the error and the execution complexity. The main conclusions are that the best algorithm choice for the implementation of a general purpose vision sensor is the Normalized 8 Point Algorithm, and the usage conditions of each method, besides the special considerations that must be observed at the interpretation of the results.

A novel solution for the localization of wireless networked systems is presented. The solution is based on cooperative estimation, inter-node ranging and strap-down inertial navigation. This approach overrides limitations that are commonly found in currently available localization/positioning solutions. Some solutions, such as GPS, make use of previously deployed infrastructure. In other methods, computations are performed in a central fusion center. In the robotics field, current localization techniques rely on a simultaneous localization and mapping, (SLAM), process, which is slow and requires sensors such as laser range finders or cameras. One of the main attributes of this research is the holistic view of the problem and a systems-engineering approach, which begins with analyzing requirements and establishing metrics for localization. The all encompassing approach provides for concurrent consideration and integration of several aspects of the localization problem, from sensor fusion algorithms for position estimation to the communication protocols required for enabling cooperative localization. As a result, a conceptual solution is presented, which is flexible, general and one that can be adapted to a variety of application scenarios. A major advantage of the solution resides in the utilization of wireless network interfaces for communications and for exteroceptive sensing. In addition, the localization solution can be seamlessly integrated into other localization schemes, which will provide faster convergence, higher accuracy and less latency. Two case-studies for developing the main aspects of cooperative localization were employed. Wireless sensor networks and multi-robot systems, composed of ground robots, provided an information base from which this research was launched. In the wireless sensor network field, novel nonlinear cooperative estimation algorithms are proposed for sequential position estimation. In the field of multi-robot systems the issues of mobility and proprioception, which uses inertial measurement systems for estimating motion, are contemplated. Motion information, in conjunction with range information and communications, can be used for accurate localization and tracking of mobile nodes. A novel partitioning of the sensor fusion problem is presented, which combines an extended Kalman filter for dead-reckoning and particle filters for aiding navigation.