Visual Simultaneous Localization And Mapping (SLAM) allows for three dimensionalreconstruction from a camera’s output and simultaneous positioning of the camera withinthe reconstruction. With use cases ranging from autonomous vehicles to augmentedreality, the SLAM field has garnered interest both commercially and academically. A SLAM system performs odometry as it estimates the camera’s movement throughthe scene. The incremental estimation of odometry is not error free and exhibits driftover time with map inconsistencies as a result. Detecting the return to a previously seenplace, a loop, means that this new information regarding our position can be incorporatedto correct the trajectory retroactively. Loop detection can also facilitate relocalization ifthe system loses tracking due to e.g. heavy motion blur. This thesis proposes an odometric system making use of bundle adjustment within akeyframe based stereo SLAM application. This system is capable of detecting loops byutilizing the algorithm FAB-MAP. Two aspects of this system is evaluated, the odometryand the capability to relocate. Both of these are evaluated using the EuRoC MAV dataset,with an absolute trajectory RMS error ranging from 0.80 m to 1.70 m for the machinehall sequences. The capability to relocate is evaluated using a novel methodology that intuitively canbe interpreted. Results are given for different levels of strictness to encompass differentuse cases. The method makes use of reprojection of points seen in keyframes to definewhether a relocalization is possible or not. The system shows a capability to relocate inup to 85% of all cases when a keyframe exists that can project 90% of its points intothe current view. Errors in estimated poses were found to be correlated with the relativedistance, with errors less than 10 cm in 23% to 73% of all cases. The evaluation of the whole system is augmented with an evaluation of local imagedescriptors and pose estimation algorithms. The descriptor SIFT was found to performbest overall, but demanding to compute. BRISK was deemed the best alternative for afast yet accurate descriptor. Conclusions that can be drawn from this thesis is that FAB-MAP works well fordetecting loops as long as the addition of keyframes is handled appropriately.

<p>The goal of this work has been to improve the accuracy of a pre-existing algorithm for vehicle pose estimation, which uses intrinsic measurements of vehicle motion and measurements derived from far infrared images.</p><p>Estimating the pose of a vehicle, based on images from an on-board camera and intrinsic measurements of vehicle motion, is a problem of simultanoeus localization and mapping (SLAM), and it can be solved using the extended Kalman filter (EKF). The EKF is a causal filter, so if the pose estimation problem is to be solved off-line acausal methods are expected to increase estimation accuracy significantly. In this work the EKF has been compared with an acausal method for solving the SLAM problem called smoothing and mapping (SAM) which is an optimization based method that minimizes process and measurement noise.</p><p>Analyses of how improvements in the vehicle motion model, using a number of different model extensions, affects accuracy of pose estimates have also been performed.</p>

In this master thesis a visual odometry system is implemented and explained. Visual odometry is a technique, which could be used on autonomous vehicles to determine its current position and is preferably used indoors when GPS is notworking. The only input to the system are the images from a stereo camera and the output is the current location given in relative position. In the C++ implementation, image features are found and matched between the stereo images and the previous stereo pair, which gives a range of 150-250 verified feature matchings. The image coordinates are triangulated into a 3D-point cloud. The distance between two subsequent point clouds is minimized with respect to rigid transformations, which gives the motion described with six parameters, three for the translation and three for the rotation. Noise in the image coordinates gives reconstruction errors which makes the motion estimation very sensitive. The results from six experiments show that the weakness of the system is the ability to distinguish rotations from translations. However, if the system has additional knowledge of how it is moving, the minimization can be done with only three parameters and the system can estimate its position with less than 5 % error.

The goal of this work has been to improve the accuracy of a pre-existing algorithm for vehicle pose estimation, which uses intrinsic measurements of vehicle motion and measurements derived from far infrared images. Estimating the pose of a vehicle, based on images from an on-board camera and intrinsic measurements of vehicle motion, is a problem of simultanoeus localization and mapping (SLAM), and it can be solved using the extended Kalman filter (EKF). The EKF is a causal filter, so if the pose estimation problem is to be solved off-line acausal methods are expected to increase estimation accuracy significantly. In this work the EKF has been compared with an acausal method for solving the SLAM problem called smoothing and mapping (SAM) which is an optimization based method that minimizes process and measurement noise. Analyses of how improvements in the vehicle motion model, using a number of different model extensions, affects accuracy of pose estimates have also been performed.

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.