Dans le cadre de la robotique mobile extérieure, les notions de perception et de localisation sont essentielles au fonctionnement autonome d’un véhicule. Les objectifs de ce travail de thèse sont multiples et mènent vers un but de localisation et de cartographie simultanée d’un environnement extérieur dynamique avec détection et suivi d’objet mobiles (SLAMMOT) à l’aide d’un unique capteur extéroceptif tournant de type radar dans des conditions de circulation dites "réalistes", c’est-à-dire à haute vitesse soit environ 30 km/h. Il est à noter qu’à de telles vitesses, les données acquises par un capteur tournant son corrompues par le déplacement propre du véhicule. Cette distorsion, habituellement considérée comme une perturbation, est analysée ici comme une source d’information. Cette étude vise également à évaluer les potentialités d’un capteur radar de type FMCW (onde continue modulée en fréquence) pour le fonctionnement d’un véhicule robotique autonome. Nous avons ainsi proposé différentes contributions : – une correction de la distorsion à la volée par capteurs proprioceptifs qui a conduit à une application de localisation et de cartographie simultanées (SLAM), – une méthode d’évaluation de résultats de SLAM basées segment, – une considération de la distorsion des données dans un but proprioceptif menant à une application SLAM, – un principe d’odométrie fondée sur les données Doppler propres au capteur radar, – une méthode de détection et de pistage d’objets mobiles : DATMO avec un unique radar. / In outdoor robotic context, notion of perception and localization is essential for an autonomous navigation of a mobile robot. The objectives of this PhD are multiple and tend to develop a simultaneous localization and mapping approach in a dynamic outdoor environment with detection and tracking of moving objects (SLAMMOT) with a unique exteroceptive radar sensor in real driving conditions, around 30 km/h. At such high speed, data obtained with a rotating range sensor are corrupted by the own vehicle displacement. This distortion, usually considered as a disturbance, is analyzed here as a source of information. This study explores radar frequency modulated continuous wave (FMCW) technology potential for mobile robotics in extended outdoor environment. In this work, we propose : – a distortion correction on-the-fly with proprioceptive sensors in order to realize a localization and mapping application (SLAM), – a line based SLAM evaluation method, – a consideration of distortion in a proprioceptive purpose for localization and mapping, – an odometry principle based on Doppler velocimetry provided by radar sensor, – a detection and tracking of mobile objects : DATMO, with a unique radar sensor.

This thesis deals with knowledge discovery in spatio-temporal data, which is currently a rapidly evolving area of research in information technology. First, it describes the general principles of knowledge discovery, then, after a brief introduction to mining in the temporal and spatial data, it focuses on the overview and description of existing methods for mining in spatio-temporal data. It focuses, in particular, on moving objects data in the form of trajectories with an emphasis on the methods for trajectory outlier detection. The next part of the thesis deals with the process of implementation of the trajectory outlier detection algorithm called TOP-EYE. In order to testing, validation and possibility of using this algorithm is designed and implemented an application for trajectory outlier detection. The algorithm is experimentally evaluated on two different data sets.

Title: Image Deblurring in Demanding Conditions Author: Jan Kotera Department: Institute of Information Theory and Automation, Czech Academy of Sciences Supervisor: Doc. Ing. Filip Šroubek, Ph.D., DSc., Institute of Information Theory and Automation, Czech Academy of Sciences Abstract: Image deblurring is a computer vision task consisting of removing blur from image, the objective is to recover the sharp image corresponding to the blurred input. If the nature and shape of the blur is unknown and must be estimated from the input image, image deblurring is called blind and naturally presents a more difficult problem. This thesis focuses on two primary topics related to blind image deblurring. In the first part we work with the standard image deblurring based on the common convolution blur model and present a method of increasing robustness of the deblur- ring to phenomena violating the linear acquisition model, such as for example inten- sity clipping caused by sensor saturation in overexposed pixels. If not properly taken care of, these effects significantly decrease accuracy of the blur estimation and visual quality of the restored image. Rather than tailoring the deblurring method explicitly for each particular type of acquisition model violation we present a general approach based on flexible automatic...