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.

I detta examensarbete har ett system för navigering och styrning av ett autonomt fordon implementerats. Syftet med detta arbete är att vidareutveckla fordonet som ska användas vid utvärdering av banplaneringsalgoritmer och studier av andra autonomifunktioner. Med hjälp av olika sensormodeller och sensorkonfigurationer går det även att utvärdera olika strategier för navigering. Arbetet har utförts utgående från en given plattform där fordonet endast använder sig av enkla ultraljudssensorer samt pulsgivare på hjulen för att mäta förflyttningar. Fordonet kan även autonomt navigera samt följa en enklare given bana i en känd omgivning. Systemet använder ett partikelfilter för att skatta fordonets tillstånd med hjälp av modeller för fordon och sensorer. Arbetet är en fortsättning på projektet Collision Avoidance för autonomt fordon som genomfördes vid Linköpings universitet våren 2005. / In this thesis a system for navigation and control of an autonomous ground vehicle has been implemented. The purpose of this thesis is to further develop the vehicle that is to be used in studies and evaluations of path planning algorithms as well as studies of other autonomy functions. With different sensor configurations and sensor models it is also possible to evaluate different strategies for navigation. The work has been performed using a given platform which measures the vehicle’s movement using only simple ultrasonic sensors and pulse encoders. The vehicle is able to navigate autonomously and follow a simple path in a known environment. The state estimation is performed using a particle filter. The work is a continuation of a previous project, Collision Avoidance för autonomt fordon, at Linköpings University in the spring of 2005.

<p>The goal of this thesis is to make an unmanned ground vehicle (UGV) follow a given reference trajectory, without colliding with obstacles in its way. This thesis will especially focus on modeling and controlling the UGV, which is based on the power wheelchair Trax from Permobil.</p><p>In order to make the UGV follow a given reference trajectory without colliding, it is crucial to know the position of the UGV at all times. Odometry is used to estimate the position of the UGV relative a starting point. For the odometry to work in a satisfying way, parameters such as wheel radii and wheel base have to be calibrated. Two control signals are used to control the motion of the UGV, one to control the speed and one to control the steering angles of the two front wheels. By modeling the motion of the UGV as a function of the control signals, the motion can be predicted. A path following algorithm is developed in order to make the UGV navigate by maps. The maps are given in advance and do not contain any obstacles. A method to handle obstacles that comes in the way is presented.</p>

The goal of this thesis is to make an unmanned ground vehicle (UGV) follow a given reference trajectory, without colliding with obstacles in its way. This thesis will especially focus on modeling and controlling the UGV, which is based on the power wheelchair Trax from Permobil. In order to make the UGV follow a given reference trajectory without colliding, it is crucial to know the position of the UGV at all times. Odometry is used to estimate the position of the UGV relative a starting point. For the odometry to work in a satisfying way, parameters such as wheel radii and wheel base have to be calibrated. Two control signals are used to control the motion of the UGV, one to control the speed and one to control the steering angles of the two front wheels. By modeling the motion of the UGV as a function of the control signals, the motion can be predicted. A path following algorithm is developed in order to make the UGV navigate by maps. The maps are given in advance and do not contain any obstacles. A method to handle obstacles that comes in the way is presented.

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.

In this work, we propose a depth estimation system based on image sequence and odometry information. The key idea is that depth estimation is decoupled from pose estimation. Such approach results in multipurpose system applicable on different robot platforms and for different depth estimation related problems. Our implementation uses various filtration techniques, operates real-time and provides appropriate results. Although the system was aimed at and tested on drone platform, it can be well used on any other type of autonomous vehicle that provides odometry information and video output.

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.