Modern cars can be bought with camera systems that watch the road ahead. They can be used for many purposes, one use is to alert the driver when other cars are in the path of collision. If the warning system is to be reliable, the input data must be correct. One input can be the depth image from a stereo camera system; one reason for the depth image to be wrong is if the vergence angle between the cameras are erroneously calibrated. Even if the calibration is accurate from production there's a risk that the vergence changes due to temperature variations when the car is started. This thesis proposes one solution for short-time live calibration of a stereo camera system; where the speedometer data available on the CAN-bus is used as reference. The motion of the car is estimated using visual odometry, which will be affected by any errors in the calibration. The vergence angle is then altered virtually until the estimated speed is equal to the reference speed. The method is analyzed for noise and tested on real data. It is shown that detection of calibration errors down to 0.01 degrees is possible under certain circumstances using the proposed method.

abstract: The exponential rise in unmanned aerial vehicles has necessitated the need for accurate pose estimation under any extreme conditions. Visual Odometry (VO) is the estimation of position and orientation of a vehicle based on analysis of a sequence of images captured from a camera mounted on it. VO offers a cheap and relatively accurate alternative to conventional odometry techniques like wheel odometry, inertial measurement systems and global positioning system (GPS). This thesis implements and analyzes the performance of a two camera based VO called Stereo based visual odometry (SVO) in presence of various deterrent factors like shadows, extremely bright outdoors, wet conditions etc... To allow the implementation of VO on any generic vehicle, a discussion on porting of the VO algorithm to android handsets is presented too. The SVO is implemented in three steps. In the first step, a dense disparity map for a scene is computed. To achieve this we utilize sum of absolute differences technique for stereo matching on rectified and pre-filtered stereo frames. Epipolar geometry is used to simplify the matching problem. The second step involves feature detection and temporal matching. Feature detection is carried out by Harris corner detector. These features are matched between two consecutive frames using the Lucas-Kanade feature tracker. The 3D co-ordinates of these matched set of features are computed from the disparity map obtained from the first step and are mapped into each other by a translation and a rotation. The rotation and translation is computed using least squares minimization with the aid of Singular Value Decomposition. Random Sample Consensus (RANSAC) is used for outlier detection. This comprises the third step. The accuracy of the algorithm is quantified based on the final position error, which is the difference between the final position computed by the SVO algorithm and the final ground truth position as obtained from the GPS. The SVO showed an error of around 1% under normal conditions for a path length of 60 m and around 3% in bright conditions for a path length of 130 m. The algorithm suffered in presence of shadows and vibrations, with errors of around 15% and path lengths of 20 m and 100 m respectively. / Dissertation/Thesis / M.S. Electrical Engineering 2010

Extrair informação de profundidade a partir de imagens bidimensionais é um importante problema na área de visão computacional. Diversas aplicações se beneficiam desta classe de algoritmos tais como: robótica, a indústria de entretenimento, aplicações médicas para diagnóstico e confecção de próteses e até mesmo exploração interplanetária. Esta aplicação pode ser dividida em duas etapas interdependentes: a estimação da posição e orientação da câmera no momento em que a imagem foi gerada, e a estimativa da estrutura tridimensional da cena. Este trabalho foca em técnicas de visão computacional usadas para estimar a trajetória de um veículo equipado com uma câmera, problema conhecido como odometria visual. Para obter medidas objetivas de eficiência e precisão, e poder comparar os resultados obtidos com o estado da arte, uma base de dados de alta precisão, bastante utilizada pela comunidade científica foi utilizada. No curso deste trabalho novas técnicas para rastreamento de detalhes, estimativa de posição de câmera, cálculo de posição 3D de pontos e recuperação de escala são propostos. Os resultados alcançados superam os mais bem ranqueados trabalhos na base de dados escolhida até o momento da publicação desta tese. / Recovering three-dimensional information from bi-dimensional images is an important problem in computer vision that finds several applications in our society. Robotics, entertainment industry, medical diagnose and prosthesis, and even interplanetary exploration benefit from vision based 3D estimation. The problem can be divided in two interdependent operations: estimating the camera position and orientation when each image was produced, and estimating the 3D scene structure. This work focuses on computer vision techniques, used to estimate the trajectory of a vehicle equipped camera, a problem known as visual odometry. In order to provide an objective measure of estimation efficiency and to compare the achieved results to the state-of-the-art works in visual odometry a high precision popular dataset was selected and used. In the course of this work new techniques for image feature tracking, camera pose estimation, point 3D position calculation and scale recovery are proposed. The achieved results outperform the best ranked results in the popular chosen dataset.

The three-dimensional mapping environments has been intensively studied in the last decade. Among the benefits of this research topic is possible to highlight the addition of autonomy for car or even drones. The three-dimensional representation also allows viewing of a given scenario iteratively and with greater detail. However, until the time of this work was not found one framework to present in detail the implementation of algorithms to perform 3D mapping outdoor approaching a real-time processing. In view of this, in this work we developed a framework with the main stages of three-dimensional reconstruction. Therefore, stereoscopy was chosen as a technique for acquiring the depth information of the scene. In addition, this study evaluated four algorithms depth map generation, where it was possible to achieve the rate of 9 frames per second. / O mapeamento tridimensional de ambientes tem sido intensivamente estudado na última década. Entre os benefícios deste tema de pesquisa é possível destacar adição de autonomia á automóveis ou mesmo drones. A representação tridimensional também permite a visualização de um dado cenário de modo iterativo e com maior riqueza de detalhes. No entanto, até o momento da elaboração deste trabalho não foi encontrado um framework que apresente em detalhes a implementação de algoritmos para realização do mapeamento 3D de ambientes externos que se aproximasse de um processamento em tempo real. Diante disto, neste trabalho foi desenvolvido um framework com as principais etapas de reconstrução tridimensional. Para tanto, a estereoscopia foi escolhida como técnica para a aquisição da informação de profundidade do cenário. Além disto, neste trabalho foram avaliados 4 algoritmos de geração do mapa de profundidade, onde foi possível atingir a taxa de 9 quadros por segundo.

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.

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.

This thesis deals with algorithms of visual odometry and its application on the experimental vehicle Car4. The first part contains different researches in this area on which the solution process is based. Next chapters introduce theoretical design and ideas of monocular and stereo visual odometry algorithms. The third part deals with the implementation in the software MATLAB with the use of Image processing toolbox. After tests done and based on real data, the chosen algorithm is applied to the vehicle Car4 used in practical conditions of interior and exterior. The last part summarizes the results of the work and address the problems which are asociated with the application of visual obmetry algorithms.

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.