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Detection and Vizualization of Features in a Point Cloud
Kratochvíl, Jiří Jaroslav ; Mikeš, Josef (referee) ; Martišek, Dalibor (referee) ; Procházková, Jana (advisor)
The point cloud is an unorganized set of points with 3D coordinates (x, y, z) which represents a real object. These point clouds are acquired by the technology called 3D scanning. This scanning technique can be done by various methods, such as LIDAR (Light Detection And Ranging) or by utilizing recently developed 3D scanners. Point clouds can be therefore used in various applications, such as mechanical or reverse engineering, rapid prototyping, biology, nuclear physics or virtual reality. Therefore in this doctoral Ph.D. thesis, I focus on feature detection and visualization in a point cloud. These features represent parts of the object that can be described by the well--known mathematical model (lines, planes, helices etc.). The points on the sharp edges are especialy problematic for commonly used methods. Therefore, I focus on detection of these problematic points. This doctoral Ph.D. thesis presents a new algorithm for precise detection of these problematic points. Visualization of these points is done by a modified curve fitting algoritm with a new weight function that leads to better results. Each of the proposed methods were tested on real data sets and compared with contemporary published methods.
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Self-Driving of a Model Car
Hazucha, Ivan ; Šimek, Václav (referee) ; Bidlo, Michal (advisor)
The aim of this thesis is to demonstrate options for self-driving model cars, focused on local path planning methods and obstacle avoidance. As a part of the project, the model was supplemented by a computing platform Raspberry Pi and appropriate sensors. Specifically, a 2D LiDAR sensor was used for detection and measuring the distance of surrounding objects, an incremental rotary encoder for measuring the distance travelled and current speed, and a gyroscope to keep track of the vehicle's relative orientation. Subsequently, a control system was implemented. This system is able to receive and process sensor data, use it to estimate vehicle's current location, compute an optimal trajectory in an uncharted environment, and control the vehicle's actuators accordingly. The result is a functional model car able to navigate in an unknown environment and reach specified goals by following a trajectory, dynamically generated depending on the surrounding obstacles.
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Robot Localization Using OpenStreet Map
Rajnoch, Zdeněk ; Veľas, Martin (referee) ; Rozman, Jaroslav (advisor)
Goal of this thesis is localization of mobile robot in OpenStreet map segment. Robot IMU, odometry and compass sensors are used for trajectory reconstruction, which is compared to reference GPS trajectory. Extended Monte Carlo localization and clusterization are used for robot localization. Software is implemented in C++ with ROS middleware.
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Traffic Signs Detection and Localisation
Kudláč, Ondrej ; Španěl, Michal (referee) ; Veľas, Martin (advisor)
This thesis aims to design the traffic signs detection and localization system using RGB image and 3D LiDAR data leveraging the the existing solutions. Traffic sign detection is based on the shape analysis. Then, the LIDAR data are used for the localization of previously detected signs. The created solution consists of two main components: the detector and locator, each able to operate independently.
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Autonomous vehicles sensors - study search
Macejka, Kamil ; Krejsa, Jiří (referee) ; Věchet, Stanislav (advisor)
This bachelor’s thesis is aimed at current trends and hardware solutions in the field of autonomous vehicles (AVs). Firstly, the definition of AV is presented and levels of autonomy are listed according to several criteria. This is followed by a brief overview of the early stages and historical development of AVs and by an introduction of sensors needed for their operation. The following part focuses on AVs created at DARPA Grand Challenge competition, which inspire even present-day companies. Next chapter describes leading companies in the area AV development, their approach to sensor selection and placement and their main goals. Furthermore, possible real applications of AVs and current pilot projects are presented. In the last part, results of this research are used to design a custom experimental AV.
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Injection locked ring oscillator design for application in Direct Time of Flight LIDAR
Fránek, Jakub ; Háze, Jiří (referee) ; Kledrowetz, Vilém (advisor)
Diplomová práce přibližuje systémy LIDAR přímo měřící čas průletu a časově digitální převodníky určené k použití v těchto systémech. Představuje problematiku distribuce hodinových signálů napříč soubory časově digitálních převodníků v LIDAR systémech a věnuje se jednomu z nových řešení této problematiky, které je založené na injekcí zavěšených oscilátorech. Technika injekčního zavěšení oscilátorů je důkladně matematicky popsána. V programu Matlab byl vytvořen simulační model injekcí zavěšeného kruhového oscilátoru, který potvrzuje správnost uvedených analytických predikcí. Ve výrobní technologii ONK65 byl navržen injekcí zavěšený kruhový oscilátor stabilizovaný pomocí smyčky závěsu zpoždění, určený pro implementaci časově digitálního převodníku pro systém LIDAR. Navržený injekcí zavěšený kruhový oscilátor byl verifikován počítačovými simulacemi zohledňujícími vliv procesních, napěťových i teplotních variací. Oscilátor poskytuje specifikované časové rozlišení 50 pikosekund a dosahuje dvakrát nižší hodnoty fázového neklidu než ekvivalentní volnoběžný oscilátor v dané technologii.
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Comparison of technologies for vehicle passage detection
Mareková, Martina ; Frolka, Jakub (referee) ; Krajsa, Ondřej (advisor)
System for vehicle detection and speed measurement system is an important part in traffic control as well as in providing data for intelligent and automated traffic signs. These technologies are divided into two types: intrusive and non-intrusive methods. Traditional traffic control is based on inductive loops. This method is intrusive and therefore requires intervention under the road and higher maintenance requirements. We can avoid this problem with non-intrusive methods, including video analysis, microwave radars, geomagnetic radars, weighing sensors and laser sensors, but these are more expensive alternatives. By implementing radars we performed measurements on road sections, on the basis of which the output data from microwave, weigh-in-motion sensors and induction loops were displayed. We receive processed and unprocessed output data of measurements from each sensors. For processing of those data is created software solution for graph rendering and direct measurement of the sensor accuracy in comparison with competing technology. The results of the measurements showed that the efficiency of the individually implemented sensors is sufficient for use in traffic control. As well we can consider the implementation of microwave radars and cameras for video analysis as a replacement for the conventional method of induction loops.
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Uncertainty of vehicle dimension measurement using 2D lidar
Želinský, David ; Richter, Miloslav (referee) ; Šedivá, Soňa (advisor)
This diploma thesis deals with the explanation of terms such as measurement error and uncertainty of measurement. It also introduces the term LiDAR, describes its construction, how it works and what it can be used for. The information from the theoretical part of this thesis is then used to analyze the uncertainties of measuring the dimensions of vehicles using 2D LiDAR. The individual measurement uncertainties are explained and calculated on an example. Furthermore, a LabVIEW program is created, which calculates the dimensions of the vehicle and the uncertainties of these dimensions from measured data. Finally, the results of the program are compared with real dimensions. The thesis is elaborated for the company Cross Zlín.
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Road Detection for Autonomous Car
Komora, Matúš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis deals with detection of the road adjacent to an autonomous vehicle. The road is recognition is based on the Velodyne LiDAR laser radar data. An existing solution is used and extended by machine learning - a Support Vector Machine with online learning. The thesis evaluates the existing solution and the new one using a KITTI dataset. The reliability of the road recognition is then computed using F-measure.
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