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This study’s aim was to investigate the mapping part of Simultaneous Localisation And Mapping (SLAM) in indoor environments containing error sources relevant to two types of sensors. The sensors used were an Intel Realsense depth camera and an RPlidar Light Detection AndRanging (LiDAR). Both cameras and LiDARs are frequently used as exteroceptive sensors in SLAM. Cameras typically struggle with strong light in the environment, and LiDARs struggle with reflective surfaces. Therefore, this study investigated the possibility of using a neural network to detect an error in either sensors’ data caused by mentioned error sources. The network identified which sensor produced erroneous data. The sensor fusion algorithm momentarily excluded said sensor’s data, consequently, improving the mapping quality when possible. The quantitative results showed no significant difference in the measured mean squared error and structural similarity between the final maps generated with and without the network, when compared to the ground truth. However, the qualitative analysis showed some advantages with using the network. Many of the camera’s errors were filtered out with the neural network, and led to a more accurate continuous mapping than without the network implemented. The conclusion was that a neural network can to a limited extent recognise the sensors’ data errors, but only the camera data benefited from the proposed solution. The study also produced important findings from the implementation which are presented. Future work recommendations include neural network optimisation, sensor selection, and sensor fusion implementation. / Denna studie undersökte kartläggningen i Simultaneous Localisation And Mapping (SLAM) problem, i kontexten av två sensorers felkällor. Sensorerna som användes var en Intel Realsense djupseende kamera samt en LiDAR fran RPlidar. Både kameror och LiDARs är vanliga sensorer i SLAM system, och båda har olika typer av felkällor. Kameror är typiskt känsliga för mycket starkt ljus, medan LiDARs har svårt med reflekterande ytor. Med detta som bakgrund har denna studie undersökt möjligheten att implementera ett neuralt nätverk för att detektera när varje sensor är utsatt för en felkälla (och därmed ger fel data). Nätverkets klassificering används sedan för att i varje tidssteg exkludera den sensors data som det är fel på för att förbättra kartläggningen. De qvantitativa resultaten visade ingen signifikant skillnad mellan kartorna genererade med nätverket och de utan nätverket. Dock visade den kvalitativa analysen att det finns vissa fördelar med att använda det neutrala nätverket. Manga av kamerans fel blev korrigerade när nätverket var implementerat, vilket ledde till mer korrekta kartor under kontinuerlig körning. Slutsatsen blev att ett nätverk kan bli tränat för att identifiera fel i datan, men att kameran drar mest nytta av det. Studien producerade även sekundara resultat som också redovisas. Slutligen rekommenderas optimering av nätverket, val av sensorer, samt uppdaterad algoritm för sensor fusionen som möjliga områden till fortsatt forskning inom området.

This thesis presents an estimator to assist or replace a fighter aircraft’s air datasystem (ADS). The estimator is based on machine learning and LSTM neuralnetworks and uses the statistical correlation between states to estimate the angleof attack, angle of sideslip and Mach number using only the internal sensorsof the aircraft. The model is trained and extensively tested on a fighter jetsimulation model and shows promising results. The methodology and accuracyof the estimator are discussed, together with how a real-world implementationwould work. The estimators presented should act as a proof of concept of thepower of neural networks in state estimation, whilst the report discusses theirstrengths and weaknesses. The estimators can estimate the three targets wellin a vast envelope of altitudes, speeds, winds and manoeuvres. However, thetechnology is quite far from real-world implementation as it lacks transparencybut shows promising potential for future development. / Det här examensarbetet presenterar en estimator för att hjälpa eller ersätta ettstridsflygplans luftdatasystem (ADS). Estimatorn är baserad på maskininlärningoch LSTM neurala nätverk och använder statistisk korrelation mellan tillstånd föratt uppskatta anfallsvinkeln, sidglidningsvinkel och Mach-tal endast med hjälpav flygplanets interna sensorer. Modellen är tränad och utförligt testad på ensimuleringsmodell för stridsflygplan och visar lovande resultat. Estimatornsmetodik och noggrannhet diskuteras, tillsammans med hur en implementeringi verkligheten skulle fungera. De presenterade estimatorerna bör fungera somett “proof of concept” för kraften hos neurala nätverk för tillståndsuppskattning,medan rapporten diskuterar deras styrkor och svagheter. Estimatorerna kanuppskatta de tre tillstånden väl i ett stort spektra av altituder, hastigheter, vindaroch manövrar. Tekniken är dock ganska långt ifrån en verklig implementeringeftersom den saknar transparens, men visar lovande potential för framtidautveckling.

In some dense urban environments (e.g., a street with tall buildings around), vehicle localization result provided by Global Positioning System (GPS) receiver might not be accurate or even unavailable due to signal reflection (multi-path) or poor satellite visibility. In order to improve the accuracy and robustness of assisted navigation systems so as to guarantee driving security and service continuity on road, a vehicle localization approach is presented in this thesis by taking use of the redundancy and complementarities of multiple sensors. At first, GPS localization method is complemented by onboard dead-reckoning (DR) method (inertial measurement unit, odometer, gyroscope), stereovision based visual odometry method, horizontal laser range finder (LRF) based scan alignment method, and a 2D GIS road network map based map-matching method to provide a coarse vehicle pose estimation. A sensor selection step is applied to validate the coherence of the observations from multiple sensors, only information provided by the validated sensors are combined under a loosely coupled probabilistic framework with an information filter. Then, if GPS receivers encounter long term outages, the accumulated localization error of DR-only method is proposed to be bounded by adding a GIS building map layer. Two onboard LRF systems (a horizontal LRF and a vertical LRF) are mounted on the roof of the vehicle and used to detect building facades in urban environment. The detected building facades are projected onto the 2D ground plane and associated with the GIS building map layer to correct the vehicle pose error, especially for the lateral error. The extracted facade landmarks from the vertical LRF scan are stored in a new GIS map layer. The proposed approach is tested and evaluated with real data sequences. Experimental results with real data show that fusion of the stereoscopic system and LRF can continue to localize the vehicle during GPS outages in short period and to correct the GPS positioning error such as GPS jumps; the road map can help to obtain an approximate estimation of the vehicle position by projecting the vehicle position on the corresponding road segment; and the integration of the building information can help to refine the initial pose estimation when GPS signals are lost for long time.

Este trabalho apresenta um sistema de auxilio à navegação autônoma para veículos terrestres com foco em ambientes estruturados em um cenário agrícola. É gerada a estimativa das posições dos obstáculos baseado na fusão das detecções provenientes do processamento dos dados de duas câmeras, uma estéreo e outra térmica. Foram desenvolvidos três módulos de detecção de obstáculos. O primeiro módulo utiliza imagens monoculares da câmera estéreo para detectar novidades no ambiente através da comparação do estado atual com o estado anterior. O segundo módulo utiliza a técnica Stixel para delimitar os obstáculos acima do plano do chão. Por fim, o terceiro módulo utiliza as imagens térmicas para encontrar assinaturas que evidenciem a presença de obstáculo. Os módulos de detecção são fundidos utilizando a Teoria de Dempster-Shafer que fornece a estimativa da presença de obstáculos no ambiente. Os experimentos foram executados em ambiente agrícola real. Foi executada a validação do sistema em cenários bem iluminados, com terreno irregular e com obstáculos diversos. O sistema apresentou um desempenho satisfatório tendo em vista a utilização de uma abordagem baseada em apenas três módulos de detecção com metodologias que não tem por objetivo priorizar a confirmação de obstáculos, mas sim a busca de novos obstáculos. Nesta dissertação são apresentados os principais componentes de um sistema de detecção de obstáculos e as etapas necessárias para a sua concepção, assim como resultados de experimentos com o uso de um veículo real. / This work presents a support system to the autonomous navigation for ground vehicles with focus on structured environments in an agricultural scenario. The estimated obstacle positions are generated based on the fusion of the detections from the processing of data from two cameras, one stereo and other thermal. Three modules obstacle detection have been developed. The first module uses monocular images of the stereo camera to detect novelties in the environment by comparing the current state with the previous state. The second module uses Stixel technique to delimit the obstacles above the ground plane. Finally, the third module uses thermal images to find signatures that reveal the presence of obstacle. The detection modules are fused using the Dempster-Shafer theory that provides an estimate of the presence of obstacles in the environment. The experiments were executed in real agricultural environment. System validation was performed in well-lit scenarios, with uneven terrain and different obstacles. The system showed satisfactory performance considering the use of an approach based on only three detection modules with methods that do not prioritize obstacle confirmation, but the search for new ones. This dissertation presents the main components of an obstacle detection system and the necessary steps for its design as well as results of experiments with the use of a real vehicle.

A utilização de sistemas de controle para estabilidade de conjuntos de barras para pulverizadores agrícolas é uma tendência devida principalmente aos problemas ambientais e de custo. Neste trabalho, o comportamento dinâmico de um pulverizador de arrasto é analisado através de um modelo não linear, obtido através de técnicas de modelagem de sistemas multicorpos utilizando-se o programa ADAMS. Foram utilizadas duas estratégias de controle PID e \"fuzzy\" a partir de medidas obtidas com fusão de sensores. A estratégia de controle clássica PID foi desenvolvida e implementada no modelo não linear no ADAMS através de ferramentas internas existentes no programa. Já a estratégia \"fuzzy\" foi desenvolvida e implementada no modelo não linear no ADAMS através da técnica de co-simulação ADAMS/Matlab. O comportamento dos sistemas de controle foi investigado através de simulação computacional. Foram testados alguns tipos de entradas (entrada degrau, entrada harmônica, entrada randômica e entrada randômica com descontinuidades bruscas). Em todas as simulações os resultados obtidos com os sistemas de controles ativos mostraram melhor estabilidade do conjunto de barras. Entre as leis de controle implementadas (PID e \"fuzzy\") não houve grandes diferenças entre as oscilações da barra exceto na entrada do tipo randômica com descontinuidades bruscas. Neste caso a lei de controle \"fuzzy\" apresentou uma grande melhoria com boa atenuação das oscilações do conjunto de barras quando comparadas com a aplicação do sistema de controle PID. / The use of active control systems for stability of booms in agricultural sprayers trend is mainly due to the environmental and costs question. In this work, the dynamic behavior of a trailed sprayer is analyzed using a nonlinear model, obtained through techniques of modeling multibody systems using the ADAMS. It is used two active control strategies, PID classical control and fuzzy, with measured data from sensor fusion. The classical PID control strategy was developed and implemented in a nonlinear model on ADAMS software using existing tools built into the program. Fuzzy was another strategy developed and implemented in the nonlinear model on ADAMS software using a technique of co-simulation ADAMS/Matlab. The behavior of control systems was investigated through computer simulation. It was tested some types of inputs (step input, harmonic input, random input and random input with abrupt discontinuities). All simulations data obtained from the applications of active systems showed better stability for boom assembly. Among the implemented two active control laws (PID and \"fuzzy\") there were no significant differences between the oscillations attenuation of the boom, except with the random input with abrupt discontinuities. wherein this case the application of the active control \"fuzzy\" strategy developed better stability on boom than the application of PID control.

This thesis represents Bayesian joint audio-visual tracking for the 3D locations of multiple people and a current speaker in a real conference environment. To achieve this objective, it focuses on several different research interests, such as acoustic-feature detection, visual-feature detection, a non-linear Bayesian framework, data association, and sensor fusion. As acoustic-feature detection, time-delay-of-arrival~(TDOA) estimation is used for multiple source detection. Localization performance using TDOAs is also analyzed according to different configurations of microphones. As a visual-feature detection, Viola-Jones face detection is used to initialize the locations of unknown multiple objects. Then, a corner feature, based on the results from the Viola-Jones face detection, is used for motion detection for robust objects. Simple point-to-line correspondences between multiple cameras using fundamental matrices are used to determine which features are more robust. As a method for data association and sensor fusion, Monte-Carlo JPDAF and a data association with IPPF~(DA-IPPF) are implemented in the framework of particle filtering. Three different tracking scenarios of acoustic source tracking, visual source tracking, and joint acoustic-visual source tracking are represented using the proposed algorithms. Finally the real-time implementation of this joint acoustic-visual tracking system using a PC, four cameras, and six microphones is addressed with two parts of system implementation and real-time processing.

[pt] Quadrirrotores são veículos aéreos que possuem quatro rotores fixos e orientados na direção vertical. Devido à sua simplicidade mecânica frente aos helicópteros tradicionais, os mesmos têm se tornado cada vez mais populares nos meios de pesquisa, militares e, mais recentemente, industriais. Essa topologia de veículo data do início do século XX mas o desenvolvimento em escala só foi possível após a recente evolução e miniaturização dos sistemas eletrônicos embarcados, dos motores elétricos e das baterias. A movimentação desses veículos no espaço é possível graças à sua inclinação em relação ao solo e, para tal, é imprescindível obter e controlar corretamente a atitude do mesmo. As unidades de medidas inerciais (IMU) surgiram como uma solução para esse problema. Através da fusão dos dados obtidos com os sensores presentes nessas centrais (acelerômetros, girômetros e magnetômetro) é possível estimar a atitude do veículo. O presente trabalho apresenta soluções tanto para a estimativa quanto para o controle de atitude de quadrirrotor. Os modelos matemáticos desenvolvidos são validados em simulações numéricas e em testes experimentais. O objetivo é que as soluções propostas apresentem resultados positivos para que possam ser empregadas nos quadrirrotores em escala. / [en] Quadrotors are vehicles that have four fixed rotors in the vertical direction. Due to its mechanical simplicity compared to traditional helicopters, these vehicles have become increasingly popular in the research, military and, more recently, industrial fields. This type of vehicle first appeared in the early twentieth century, but the development of small-scale models was only possible after the recent evolution and miniaturization of embedded electronics, electric motors and batteries. A Quadrotor can fly in any direction by changing its inclination relative to the ground, so it is essential to calculate and properly adjust its attitude. The inertial measurement units (IMU) emerged as one solution to this problem. By merging the IMU sensors data, it is possible to estimate the vehicle s attitude. This dissertation presents solutions for both the estimation and the control of the vehicle s attitude. The developed mathematical models are validated with numerical simulations and experimental tests. The goal is that the presented solutions give enough good results so they can be used in small-scale Quadrotors.

Made available in DSpace on 2014-12-17T14:55:06Z (GMT). No. of bitstreams: 1 GutembergSS.pdf: 569123 bytes, checksum: 6f85b5ee47010d2d331986f17689304b (MD5) Previous issue date: 2008-05-30 / This work presents a cooperative navigation systemof a humanoid robot and a wheeled robot using visual information, aiming to navigate the non-instrumented humanoid robot using information obtained from the instrumented wheeled robot. Despite the humanoid not having sensors to its navigation, it can be remotely controlled by infra-red signals. Thus, the wheeled robot can control the humanoid positioning itself behind him and, through visual information, find it and navigate it. The location of the wheeled robot is obtained merging information from odometers and from landmarks detection, using the Extended Kalman Filter. The marks are visually detected, and their features are extracted by image processing. Parameters obtained by image processing are directly used in the Extended Kalman Filter. Thus, while the wheeled robot locates and navigates the humanoid, it also simultaneously calculates its own location and maps the environment (SLAM). The navigation is done through heuristic algorithms based on errors between the actual and desired pose for each robot. The main contribution of this work was the implementation of a cooperative navigation system for two robots based on visual information, which can be extended to other robotic applications, as the ability to control robots without interfering on its hardware, or attaching communication devices / Este trabalho apresenta um sistema de navega??o cooperativa de um rob? human?ide e um rob? com rodas usando informa??o visual, com o objetivo de efetuar a navega??o do rob? human?ide n?o instrumentado utilizando-se das informa??es obtidas do rob? com rodas instrumentado. Apesar do human?ide n?o possuir sensores para sua navega??o, pode ser remotamente controlado por sinal infravermelho. Assim, o rob? com rodas pode controlar o human?ide posicionando-se atr?s dele e, atrav?s de informa??o visual, localiz?-lo e naveg?-lo. A localiza??o do rob? com rodas ? obtida fundindo-se informa??es de odometria e detec??o de marcos utilizando o filtro de Kalman estendido. Os marcos s?o detectados visualmente, e suas caracter?sticas s?o extra?das pelo o processamento da imagem. As informa??es das caracter?sticas da imagem s?o utilizadas diretamente no filtro de Kalman estendido. Assim, enquanto o rob? com rodas localiza e navega o human?ide, realiza tamb?m sua localiza??o e o mapeamento do ambiente simultaneamente (SLAM). A navega??o ? realizada atrav?s de algoritmos heur?sticos baseados nos erros de pose entre a pose dos rob?s e a pose desejada para cada rob?. A principal contribui??o desse trabalho foi a implementa??o de um sistema de navega??o cooperativa entre dois rob?s baseados em informa??o visual, que pode ser estendido para outras aplica??es rob?ticas, dado a possibilidade de se controlar rob?s sem interferir em seu hardware, ou acoplar dispositivos de comunica??o

A utilização de sistemas de controle para estabilidade de conjuntos de barras para pulverizadores agrícolas é uma tendência devida principalmente aos problemas ambientais e de custo. Neste trabalho, o comportamento dinâmico de um pulverizador de arrasto é analisado através de um modelo não linear, obtido através de técnicas de modelagem de sistemas multicorpos utilizando-se o programa ADAMS. Foram utilizadas duas estratégias de controle PID e \"fuzzy\" a partir de medidas obtidas com fusão de sensores. A estratégia de controle clássica PID foi desenvolvida e implementada no modelo não linear no ADAMS através de ferramentas internas existentes no programa. Já a estratégia \"fuzzy\" foi desenvolvida e implementada no modelo não linear no ADAMS através da técnica de co-simulação ADAMS/Matlab. O comportamento dos sistemas de controle foi investigado através de simulação computacional. Foram testados alguns tipos de entradas (entrada degrau, entrada harmônica, entrada randômica e entrada randômica com descontinuidades bruscas). Em todas as simulações os resultados obtidos com os sistemas de controles ativos mostraram melhor estabilidade do conjunto de barras. Entre as leis de controle implementadas (PID e \"fuzzy\") não houve grandes diferenças entre as oscilações da barra exceto na entrada do tipo randômica com descontinuidades bruscas. Neste caso a lei de controle \"fuzzy\" apresentou uma grande melhoria com boa atenuação das oscilações do conjunto de barras quando comparadas com a aplicação do sistema de controle PID. / The use of active control systems for stability of booms in agricultural sprayers trend is mainly due to the environmental and costs question. In this work, the dynamic behavior of a trailed sprayer is analyzed using a nonlinear model, obtained through techniques of modeling multibody systems using the ADAMS. It is used two active control strategies, PID classical control and fuzzy, with measured data from sensor fusion. The classical PID control strategy was developed and implemented in a nonlinear model on ADAMS software using existing tools built into the program. Fuzzy was another strategy developed and implemented in the nonlinear model on ADAMS software using a technique of co-simulation ADAMS/Matlab. The behavior of control systems was investigated through computer simulation. It was tested some types of inputs (step input, harmonic input, random input and random input with abrupt discontinuities). All simulations data obtained from the applications of active systems showed better stability for boom assembly. Among the implemented two active control laws (PID and \"fuzzy\") there were no significant differences between the oscillations attenuation of the boom, except with the random input with abrupt discontinuities. wherein this case the application of the active control \"fuzzy\" strategy developed better stability on boom than the application of PID control.