Pravděpodobnostní modely pro lokalizaci bezpilotního letounu testované na reálných datech

Práca sa zaoberá problémom odhadovania stavu dynamického systému v oblasti robotiky, konkrétne bezpilotných lietajúcich robotov. Na základe dát získaných z robota navrhneme niekoľko pravdepodobnostných modelov pre odhad jeho stavu (hlavne rýchlosti a rotačných uhlov), takisto pre konfigurácie, kde jeden zo senzorov nie je dostupný. Používame Kalmanov filter a Časticový filter a zameriavame sa na učenie parametrov modelu EM algoritmom. EM algoritmus je potom upravený vzhľadom k negaussovskému rozloženiu chyby niektorých senzorov a pridaním penalizačných členov za zložitosť modelu pre lepšie fungovanie na neznámych dátach. Tieto metódy implementujeme v prostredí MATLAB a vyhodnotíme na oddelených dátach. V práci tiež analyzujeme dáta z pozemného robota a použijeme našu implementáciu Časticového filtra pre odhad jeho polohy. Powered by TCPDF (www.tcpdf.org)The thesis addresses the dynamic state estimation problem for the field of robotics, particularly for unmanned aerial vehicles (UAVs). Based on data collected from an UAV, we design several probabilistic models for estimation of its state (mainly speed and rotation angles), including the configurations where one of the sensors is not available. We use Kalman filter and Particle filter and focus on learning the model parameters using EM algorithm. The EM algorithm is then adjusted with respect to non-Gaussian density of some sensor errors and modified using model complexity penalization terms for better generalization. We implement these methods in MATLAB environment and evaluate on separate datasets. We also analyze data from a ground robot and use our implementation of Particle filter for estimation of its position. Powered by TCPDF (www.tcpdf.org)Department of Theoretical Computer Science and Mathematical LogicKatedra teoretické informatiky a matematické logikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Machine Learning for Google Android

The thesis discusses the topic of machine learning. It describes the theoretical base of the classification task and focuses on two algorithms--decision trees and Naive Bayes classifier. Using these algorithms we have implemented a library for the Android platform. The library provides the basic functionality for the classification task and it is designed with an emphasis on simplicity and efficiency, given that it is aimed for mobile devices. The functionality of the library has been tested on a large data set and its precision has been comparable to other implementations. An important part of the thesis is an application using our library. The application collects data about culture events and helps the user to filter some of them according to his or her personal preferences. As the data are obtained online from real servers, it is not only a sample demonstration, but a usable and potentially useful mobile application

Pravděpodobnostní modely pro lokalizaci bezpilotního letounu testované na reálných datech

The thesis addresses the dynamic state estimation problem for the field of robotics, particularly for unmanned aerial vehicles (UAVs). Based on data collected from an UAV, we design several probabilistic models for estimation of its state (mainly speed and rotation angles), including the configurations where one of the sensors is not available. We use Kalman filter and Particle filter and focus on learning the model parameters using EM algorithm. The EM algorithm is then adjusted with respect to non-Gaussian density of some sensor errors and modified using model complexity penalization terms for better generalization. We implement these methods in MATLAB environment and evaluate on separate datasets. We also analyze data from a ground robot and use our implementation of Particle filter for estimation of its position. Powered by TCPDF (www.tcpdf.org