Made available in DSpace on 2014-12-17T14:55:08Z (GMT). No. of bitstreams: 1 AndersonASS.pdf: 906367 bytes, checksum: 22fe3d988905f9e44afd63465e16e0df (MD5) Previous issue date: 2008-02-15 / In this work, we propose a probabilistic mapping method with the mapped environment represented through a modified occupancy grid. The main idea of the proposed method is to allow a mobile robot to construct in a systematic and incremental way the geometry of the underlying space, obtaining at the end a complete environment map. As a consequence, the robot can move in the environment in a safe way, based on a confidence value of data obtained from its perceptive system. The map is represented in a coherent way, according to its sensory data, being these noisy or not, that comes from exterior and proprioceptive sensors of the robot. Characteristic noise incorporated in the data from these sensors are treated by probabilistic modeling in such a way that their effects can be visible in the final result of the mapping process. The results of performed experiments indicate the viability of the methodology and its applicability in the area of autonomous mobile robotics, thus being an contribution to the field / Neste trabalho, propomos um m?todo de mapeamento probabil?stico com a representa??o do ambiente mapeado em uma grade de ocupa??o modificada. A id?ia principal do m?todo proposto ? deixar que um rob? m?vel construa de forma sistem?tica e incremental a geometria do seu entorno, obtendo ao final um mapa completo do ambiente. Como conseq??ncia, o rob? poder? locomover-se no seu ambiente de modo seguro, baseando-se em um ?ndice de confiabilidade dos dados colhidos do seu sistema perceptivo. O mapa ? representado de forma coerente com os dados sensoriais, sejam esses ruidosos ou n?o, oriundos dos sensores externoceptivos e proprioceptivos do rob?. Os ru?dos caracter?sticos incorporados nos dados de tais sensores s?o tratados por modelagem probabil?stica, de modo que seus efeitos possam ser vis?veis no resultado final do processo de mapeamento. Os resultados dos experimentos realizados, mostrados no presente trabalho, indicam a viabilidade desta metodologia e sua aplicabilidade na ?rea da rob?tica m?vel aut?noma, sendo assim uma contribui??o para a ?rea

Made available in DSpace on 2014-12-17T14:55:51Z (GMT). No. of bitstreams: 1 BrunoMFS_DISSERT.pdf: 2462891 bytes, checksum: b8ea846d0fcc23b0777a6002e9ba92ac (MD5) Previous issue date: 2011-02-15 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Visual Odometry is the process that estimates camera position and orientation based solely on images and in features (projections of visual landmarks present in the scene) extraced from them. With the increasing advance of Computer Vision algorithms and computer processing power, the subarea known as Structure from Motion (SFM) started to supply mathematical tools composing localization systems for robotics and Augmented Reality applications, in contrast with its initial purpose of being used in inherently offline solutions aiming 3D reconstruction and image based modelling. In that way, this work proposes a pipeline to obtain relative position featuring a previously calibrated camera as positional sensor and based entirely on models and algorithms from SFM. Techniques usually applied in camera localization systems such as Kalman filters and particle filters are not used, making unnecessary additional information like probabilistic models for camera state transition. Experiments assessing both 3D reconstruction quality and camera position estimated by the system were performed, in which image sequences captured in reallistic scenarios were processed and compared to localization data gathered from a mobile robotic platform / Odometria Visual ? o processo pelo qual consegue-se obter a posi??o e orienta??o de uma c?mera, baseado somente em imagens e consequentemente, em caracter?sticas (proje??es de marcos visuais da cena) nelas contidas. Com o avan?o nos algoritmos e no poder de processamento dos computadores, a sub?rea de Vis?o Computacional denominada de Structure from Motion (SFM) passou a fornecer ferramentas que comp?em sistemas de localiza??o visando aplica??es como rob?tica e Realidade Aumentada, em contraste com o seu prop?sito inicial de ser usada em aplica??es predominantemente offline como reconstru??o 3D e modelagem baseada em imagens. Sendo assim, este trabalho prop?e um pipeline de obten??o de posi??o relativa que tem como caracter?sticas fazer uso de uma ?nica c?mera calibrada como sensor posicional e ser baseado interamente nos modelos e algoritmos de SFM. T?cnicas usualmente presentes em sistemas de localiza??o de c?mera como filtros de Kalman e filtros de part?culas n?o s?o empregadas, dispensando que informa??es adicionais como um modelo probabil?stico de transi??o de estados para a c?mera sejam necess?rias. Experimentos foram realizados com o prop?sito de avaliar tanto a reconstru??o 3D quanto a posi??o de c?mera retornada pelo sistema, atrav?s de sequ?ncias de imagens capturadas em ambientes reais de opera??o e compara??es com um ground truth fornecido pelos dados do od?metro de uma plataforma rob?tica

The combat vehicle, CV90 has a semi-active hydraulic suspension system which uses inertial measurements for regulation to improve accessibility. To improve performance further measurements of future terrain can be used to, for example, prepare for impacts. This master's thesis investigates the ability to use existing sensors and new sensors to facilitate these measurements. Two test runs were performed, with very different conditions and outcomes. The results seem to suggest that a sweeping LIDAR was the most accurate and robust solution. However, using a very recent visual odometry algorithm, promising results were achieved using an Infra-red heat camera. Especially given that no efforts were put into adjusting parameters for that particular algorithm.

Nesta pesquisa são estudados aspectos relacionados à problemática da localização robótica, e um sistema de localização robótica é construído. Para determinação da localização de um robô móvel em relação a um mapa topológico do ambiente, é proposta uma solução determinística. Esta solução é empregada a fim de prover localização para problemas de rastreamento de posição, embora seja de interesse também a observação da eficácia, do método proposto, frente a problemas de localização global. O sistema proposto baseia-se no uso de vetores de atributos, compostos de medições momentâneas extraídas do ambiente através de sensoriamentos pertencentes à percepção do robô. Estimativas feitas a partir da odometria e leitura de sensores de ultra-som são utilizadas em conjunto nestes vetores de atributos, de forma a caracterizar as observações feitas pelo robô. Uma bússola magnética também é empregada na solução. O problema de localização é então resolvido como um problema de reconhecimento de padrões. A topologia do ambiente é conhecida, e a correlação entre cada local neste ambiente e seus atributos são armazenados através do uso de redes neurais artificiais. O sistema de localização foi avaliado de maneira experimental, em campo, em uma plataforma robótica real, e resultados promissores foram obtidos e são apresentados. / In this research aspects related to the robot localization problem have been studied. In order to determine the localization of a mobile robot in relation to a topological map of its environment, a deterministic solution has been proposed. This solution is applied to provide localization for position tracking problems, although it is also of interest to observe the performance of the proposed method applied to global localization problems. The proposed system is based on feature vectors, which are composed of momentaneous measures extracted from sensory data of the robots perception. Estimative made from odometry, sonars and magnetic compass readings are used together in these feature vectors, in order to characterize observed scenes by the robot. Thus, the localization problem is solved as a pattern recognition problem. The topology of the environment is known, and the correlation between each place of this environment and its features is stored using an artificial neural network. The localization system was experimentally evaluated, in a real robotic platform. The results obtained allow validation of the methodology.

O problema da localização consiste em estimar a posição de um robô com relação a algum referencial externo e é parte essencial de sistemas de navegação de robôs e veículos autônomos. A localização baseada em odometria visual destaca-se em relação a odometria de encoders na obtenção da rotação e direção do movimento do robô. Esse tipo de abordagem é também uma escolha atrativa para sistemas de controle de veículos autônomos em ambientes urbanos, onde a informação visual é necessária para a extração de informações semânticas de placas, semáforos e outras sinalizações. Neste contexto este trabalho propõe o desenvolvimento de um sistema de odometria visual utilizando informação visual de uma câmera monocular baseado em reconstrução 3D para estimar o posicionamento do veículo. O problema da escala absoluta, inerente ao uso de câmeras monoculares, é resolvido utilizando um conhecimento prévio da relação métrica entre os pontos da imagem e pontos do mundo em um mesmo plano. / The localization problem consists of estimating the position of the robot with regards to some external reference and it is an essential part of robots and autonomous vehicles navigation systems. Localization based on visual odometry, compared to encoder based odometry, stands out at the estimation of rotation and direction of the movement. This kind of approach is an interesting choice for vehicle control systems in urban environment, where the visual information is mandatory for the extraction of semantic information contained in the street signs and marks. In this context this project propose the development of a visual odometry system based on structure from motion using visual information acquired from a monocular camera to estimate the vehicle pose. The absolute scale problem, inherent with the use of monocular cameras, is achieved using som previous known information regarding the metric relation between image points and points lying on a same world plane.

No description available.

Aim of this thesis is to design and construct odometric sensors for a mobile robot with Ackermann steering Bender 2 and to design a mathematical model which would evaluate the the trajectory of the robot using measured data of these sensors. The first part summarizes theoretical knowledge, while the second, the practical part, describes the design of the front axle, the design and the operating software of the front encoders and the odometric models. The last part deals with the processing and evaluation of the measured data.

The aim of this thesis is to create an experimental application for manipulating virtual objects in the augmented reality using an tablet for controlling the robotic arm. There is created various ways of manipulating virtual objects for their translation, rotation, and scale change. These methods are tested on several users and compared within their usability. The application allows you to send the position of virtual object changes to the PR2 robot arm and simulate manipulation of virtual objects with augmented reality.

Tato diplomová práce se zabývá tvorbou fúze pozičních dat z existující realtimové im- plementace vizuálního SLAMu a kolové odometrie. Výsledkem spojení dat je potlačení nežádoucích chyb u každé ze zmíněných metod měření, díky čemuž je možné vytvořit přesnější 3D model zkoumaného prostředí. Práce nejprve uvádí teorií potřebnou pro zvládnutí problematiky 3D SLAMu. Dále popisuje vlastnosti použitého open source SLAM projektu a jeho jednotlivé softwarové úpravy. Následně popisuje principy spo- jení pozičních informací získaných vizuálními a odometrickými snímači, dále uvádí popis diferenciálního podvozku, který byl použit pro tvorbu kolové odometrie. Na závěr práce shrnuje výsledky dosažené datovou fúzí a srovnává je s původní přesností vizuálního SLAMu.

Cette thèse s'intéresse au problème du positionnement (position et orientation) dans un contexte de réalité augmentée et aborde spécifiquement les solutions à base de capteurs embarqués. Aujourd'hui, les systèmes de navigation vision-inertiel commencent à combler les besoins spécifiques de cette application. Néanmoins, ces systèmes se basent tous sur des corrections de trajectoire issues des informations visuelles à haute fréquence afin de pallier la rapide dérive des capteurs inertiels bas-coûts. Pour cette raison, ces méthodes sont mises en défaut lorsque l'environnement visuel est défavorable.Parallèlement, des travaux récents menés par la société Sysnav ont démontré qu'il était possible de réduire la dérive de l'intégration inertielle en exploitant le champ magnétique, grâce à un nouveau type d'UMI bas-coût composée – en plus des accéléromètres et gyromètres traditionnels – d'un réseau de magnétomètres. Néanmoins, cette méthode est également mise en défaut si des hypothèses de non-uniformité et de stationnarité du champ magnétique ne sont pas vérifiées localement autour du capteur.Nos travaux portent sur le développement d'une solution de navigation à l'estime robuste combinant toutes ces sources d'information: magnétiques, visuelles et inertielles.Nous présentons plusieurs approches pour la fusion de ces données, basées sur des méthodes de filtrage ou d’optimisation et nous développons un modèle de prédiction du champ magnétique inspiré d'approximation proposées en inertiel et permettant d’intégrer efficacement des termes magnétiques dans les méthodes d’ajustement de faisceaux. Les performances de ces différentes approches sont évaluées sur des données réelles et nous démontrons le bénéfice de la fusion de données comparées aux solutions vision-inertielles ou magnéto-inertielles. Des propriétés théoriques de ces méthodes liées à la théorie de l’invariance des estimateurs sont également étudiées. / This thesis addresses the issue of positioning in 6-DOF that arises from augmented reality applications and focuses on embedded sensors based solutions.Nowadays, the performance reached by visual-inertial navigation systems is starting to be adequate for AR applications. Nonetheless, those systems are based on position correction from visual sensors involved at a relatively high frequency to mitigate the quick drift of low-cost inertial sensors. This is a problem when the visual environment is unfavorable.In parallel, recent works have shown it was feasible to leverage magnetic field to reduce inertial integration drift thanks to a new type of low-cost sensor, which includes – in addition to the accelerometers and gyrometers – a network of magnetometers. Yet, this magnetic approach for dead-reckoning fails if stationarity and non-uniformity hypothesis on the magnetic field are unfulfilled in the vicinity of the sensor.We develop a robust dead-reckoning solution combining simultaneously information from all these sources: magnetic, visual, and inertial sensor. We present several approaches to solve for the fusion problem, using either filtering or non-linear optimization paradigm and we develop an efficient way to use magnetic error term in a classical bundle adjustment that was inspired from already used idea for inertial terms. We evaluate the performance of these estimators on data from real sensors. We demonstrate the benefits of the fusion compared to visual-inertial and magneto-inertial solutions. Finally, we study theoretical properties of the estimators that are linked to invariance theory.