Entropy - Like Estimation Technique in Mobile Robot Localization

This diploma thesis deals with a problem of the mobile robot localization in an unknown outdoor environment. The studied localization problem is based on processing observations of the robot surroundings sensed by its exteroceptors providing a set of measurements called scan. Such a scan contains a percepted features of the environment that are used in a scan-to-scan localization method. The method provides an estimation of the robot pose transformation describing the robot motion (from which the global robot pose is determined) and the precision of the estimation is influenced by a noise and outliers in the input datasets (scans). In this thesis, a new estimation technique called Least Entropy-Like (LEL) to find parameters of the transformation is studied in the context of the mobile robot localization problem. This technique has been designed to be robust to a dataset corrupted by a significant amount of outliers, and therefore, it is a promising technique to solve the localization problem. The main goal of the thesis is to evaluate and verify the performance of LEL in a serie of experiments and realistic scenarios of mobile robot localization to provide a realistic expectation of the performance in a real deployment of the method. The considered robot localization system is based on a stereoscopic camera system and extraction of features from the image using the Speeded-Up Robust Feature (SURF) detection and estimation of the feature’s depth from the disparity between the left and right images and known parameters of the cameras. In addition to evaluation of the estimation technique for outliers, the technique is evaluated also according to the quality of found correspondences between features in two consecutive scans. Moreover, a new data association method is proposed to extract only strong feature correspondences, which positively impact the performance of the LEL technique. Besides, an overview of localization techniques and their comparison is presented. Finally, the properties and discovered findings are presented in the conclusion. Velice děkuji Ing. Janu Faiglovi, Ph.D. za trpělivost, ochotu a cenné rady, jak k obsahu práce, tak správnému psaní technického textu v angličtině. Dále mu děkuji za všechen čas, který mi při konzultacích věnoval i v rámci svého osobního volna. Děkuji za jeho zapálení do problematiky, které mě pomáhalo překonat těžké chvíle, a za úžasnou pečlivost, ke které mě také vedl. Na závěr bych mu ráda poděkovala za pochopení a podporu v ostatní situacích mého života. Dále bych chtěla poděkovat bratrovi a jeho rodině za psychickou podporu. Děkuji mému přítelovi za to, že mě vždy vyslechl, což mi mnohdy pomohlo si ujasnit směrování diplomové práce. V neposlední řadě děkuji rodičům za podporu při studium a svým kamarádům ze školy, se kterými jsem sdíleli radosti i strasti při studiu.