Localization of Mobile Robot Using Multiple Sensors

Tato práce se věnuje celoživotnímu určování polohy mobilního robotu, který je vybavený různými senzory. Informace o poloze robotu a mapa jsou nezbytné pro zajištění autonomního pohybu. Cílem je implementovat metodu řešící problém zvaný Simultání lokalizace a mapování pomocí přístupu využívající Transformaci normálního rozdělení. Důraz je kladen na schopnost využít CAD výkresy prostředí jako počáteční mapu. Práce zahrnuje princip metody, popis implementace a zhodnocení výsledků, které bylo zaměřeno na rozdíly v lokalizaci a mapování s využitím CAD výkresů a bez nich.This thesis is dedicated to a lifelong localization of a mobile robot, which is equipped with the multiple sensors. The information about the robot position and the map are necessary for the autonomous movement. The goal of this thesis is implementing the method based on the Normal Distribution Transform for solving the problem called Simultaneous localization and mapping. The important requirement is the ability to use the CAD drawing of the environment as an initial map. The thesis contains the principle of the method, the description of the implementation, and the experiments evaluation. The experiments have been focused on the difference between the localization and mapping process with and without the CAD drawing

The problem of fingerprints selection for topological localization

Visual navigation is extensively used in contemporary robotics. In particular, we can mention different systems of visual landmarks. In this paper, we consider one-dimensional color panoramas. Panoramas can be used for creating fingerprints. Fingerprints give us unique identifiers for visually distinct locations by recovering statistically significant features. Also, it can be used as visual landmarks for mobile robot navigation. In this paper, we consider a method for automatic generation of fingerprints. Since a fingerprint is a circular string, different string-matching algorithms can be used for selection of fingerprints. In particular, we consider the problem of finding the consensus of circular strings under the Hamming distance metric. We propose an approach to solve the problem. In particular, we consider the center string problem, the center circular string problem, and the center circular string with fixed letters problem. We obtain an explicit reduction from the center circular string problem to the satisfiability problem. We propose a genetic algorithm for solution of the center circular string problem. Also, we propose a genetic algorithm for the prediction the effectiveness of the use of special algorithm for four circular strings

Image features for visual teach-and-repeat navigation in changing environments

We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scale- and rotation- invariance of the standard feature extractors is less important than their robustness to the mid- and long-term environment appearance changes. Therefore, we focus our evaluation on the robustness of image registration to variable lighting and naturally-occurring seasonal changes. We combine detection and description components of different image extractors and evaluate their performance on five datasets collected by mobile vehicles in three different outdoor environments over the course of one year. Moreover, we propose a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which we call GRIEF (Generated BRIEF). In terms of robustness to seasonal changes, the most promising results were achieved by the SpG/CNN and the STAR/GRIEF feature, which was slightly less robust, but faster to calculate

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Conceptual spatial representations for indoor mobile robots

We present an approach for creating conceptual representations of human-made indoor environments using mobile robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. The system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. Finally, we discuss the capabilities of the integrated system