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Collision Avoidance System for Mobile Robot
Dzuro, Daniel ; Hájek, Josef (referee) ; Herman, David (advisor)
This bachelor's thesis deals with the anti-collision system for mobile robot which is often liable to the collisions with other objects. Expensive parts of the robot could be saved thanks to the system. The main issue of the thesis was to design the system of premature reaction and it's application to the existing system which would help prevent the collision on time. Not only the breaking system was used to stop or slow down the vehicle but also the cooperation with the prediction of the vehicle's direction control was needed. Laser rangefinder and ultrasonic sonar were mainly used for this purpose.
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Visual Navigation of the Vehicle
Jaššo, Kamil ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
The thesis consists of retrieval of known solutions of autonomous vehicle navigation. Thesis further describes options for controling autonomous vehicle, and using of sensors in autonomous vehicles. From different types of sensors are selected two types, that are most suitable for visual navigation of the vehicle. Thesis describes the function and way of using these two types of sensors in the visual navigation of the vehicle. The program for obtaining and saving data from selected sensors is also part of the thesis.
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Extraction of Landscape Elements from Remote Sensing Data
Ferencz, Jakub ; Kalvoda, Petr (referee) ; Hanzl, Vlastimil (advisor)
This master thesis deals with a classification technique for an automatic detection of different land cover types from combination of high resolution imagery and LiDAR data sets. The main aim is to introduce additional post-processing method to commonly accessible quality data sets which can replace traditional mapping techniques for certain type of applications. Classification is the process of dividing the image into land cover categories which helps with continuous and up-to-date monitoring management. Nowadays, with all the technologies and software available, it is possible to replace traditional monitoring methods with more automated processes to generate accurate and cost-effective results. This project uses object-oriented image analysis (OBIA) to classify available data sets into five main land cover classes. The automate classification rule set providing overall accuracy of 88% of correctly classified land cover types was developed and evaluated in this research. Further, the transferability of developed approach was tested upon the same type of data sets within different study area with similar success – overall accuracy was 87%. Also the limitations found during the investigation procedure are discussed and brief further approach in this field is outlined.
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3D object detection from pointcloud
Červený, Patrik ; Richter, Miloslav (referee) ; Zemčík, Tomáš (advisor)
The bachelor thesis deals with the detection of 3D objects in a point cloud acquired by a LIDAR sensor. The thesis describes the sensors that collect the data for the point cloud, the methods to process the data and the final representation of the collected data, using the chosen application. By using a LIDAR sensor we detect a 3D image of the point cloud at the railway stations in Brno and with the help of the available methods for processing and application we try to describe what the sensor shows us through the data.
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System for Autonomous Navigation of Toy Car on a Race Track
Katrušák, Jaroslav ; Bidlo, Michal (referee) ; Šimek, Václav (advisor)
The goal of this project is the implementation of a system for autonomous control of a toy car model on a racetrack. First part of this project is a study of typical ways of laying out a racetrack for the movement of a model car and a follow-up study of possible ways of implementing the autonomous movement of the model on the racetrack. The next part is devoted to the hardware and software side of the implementation of the proposed system. The penultimate part presents the proposed system for obstacle detection, it's implementation and resulting properties. The last part of the project presents the final implementation of an autonomous model capable of independent driving on a set racetrack, reading signs and reaction to obstacle located on the track.
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2D Point-cloud segmentation for curve fitting
Šooš, Marek ; Krejsa, Jiří (referee) ; Králík, Jan (advisor)
The presented diploma thesis deals with the division of points into homogeneous groups. The work provides a broad overview of the current state in this topic and a brief explanation of the main segmentation methods principles. From the analysis of the articles are selected and programmed five algorithms. The work defines the principles of selected algorithms and explains their mathematical models. For each algorithm is also given a code design description. The diploma thesis also contains a cross comparison of segmentation capabilities of individual algorithms on created as well as on measured data. The results of the curves extraction are compared with each other graphically and numerically. At the end of the work is a comparison graph of time dependence on the number of points and the table that includes a mutual comparison of algorithms in specific areas.
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LIDAR and Stereocamera in Localization of Mobile Robots
Vyroubalová, Jana ; Drahanský, Martin (referee) ; Orság, Filip (advisor)
LIDAR (2D) has been widely used for mapping, localization and navigation in mobile robotics. However, its usage is limited to simple environments. This problem can be solved by adding more sensors and processing these data together. This paper explores a method how measurements from a stereo camera and LIDAR are fused to dynamical mapping. An occupancy grid map from LIDAR data is used as prerequisite and extended by a 2D grid map from stereo camera. This approach is based on the ground plane estimation in disparity map acquired from the stereo vision. For the ground plane detection, RANSAC and Least Squares methods are used. After obstacles determination, 2D occupancy map is generated. The output of this method is 2D map as a fusion of complementary maps from LIDAR and camera. Experimental results obtained from RUDA robot and MIT Stata Center Data Set are good enough to determine that this method is a benefit, although my implementation is still a prototype. In this paper, we present the applied methods, analyze the results and discuss the modifications and possible extensions to get better results.
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New features for real-time positioning system locator
Studený, Jakub ; Sekora, Jiří (referee) ; Kolářová, Jana (advisor)
The diploma thesis deals with the detection of falls and impacts, based on data obtained from inertial sensors, and by measuring the distance using a laser. The aim of this thesis is to extend the functionality of locators from Sewio. The thesis describes the procedure for designing algorithms for detection of falls and impacts. Then there is a procedure for development of hardware and software solution, for laser distance measurement by locator, together with presentation of achieved measurement results realized by locator after implementation of proposed solution. The work also emphasizes the minimization of energy consumption of individual solutions. In conclusion, there is a discussion of achieved results with evaluation of efficiency and usability of proposed solutions.
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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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