<p>Autonomous robots typically rely on internal representations of the environment, or maps, to plan and execute their tasks. Several types of maps have been proposed in the literature, and there is general consensus that different types have different advantages and limitations, and that each type is more suited to certain tasks and less to others. Because of these reasons, it is becoming common wisdom in the field of mobile robotics to use hybrid maps that integrate several representations, usually of different types. Hybrid maps provide scalability and multiple views, allowing for instance to combine robot-centered and human-centered representations. There is, however, little understanding of the general principles that can be used to combine different maps into a hybrid one, and to make it something more than the sum of its parts. There is no systematic analysis of the different ways in which different maps can be combined, and how they can be made to cooperate. This makes it difficult to evaluate and compare different systems, and precludes us from getting a clear understanding of how a hybrid map can be designed or improved.</p><p>The investigation presented in this thesis aims to contribute to fill this foundational gap, and to get a clearer understanding of the nature of hybrid maps. To help in this investigation, we develop two tools: The first one is a conceptual tool, an analytical framework in which the main ingredients of a hybrid map are described; the second one is an empirical tool, a new hybrid map that allows us to experimentally verify our claims and hypotheses.</p><p>While these tools are themselves important contributions of this thesis, our investigation has resulted in the following additional outcomes:</p><p>• A set of concepts that allow us to better understand the structure and operation of hybrid maps, and that help us to design them, compare them, identify their problems, and possibly improve them;</p><p>• The identification of the notion of synergy as the fundamental way in which component maps inside a hybrid map cooperate.</p><p>To assess the significance of these outcomes, we make and validate the following claims:</p><p>1. Our framework allows us to classify and describe existing maps in a uniform way. This claim is validated constructively by making a thorough classification of the hybrid maps reported in the literature.</p><p>2. Our framework also allows us to enhance an existing hybrid map by identifying spots for improvement. This claim is verified experimentally by modifying an existing map and evaluating its performance against the original one.</p><p>3. The notion of synergy plays an important role in hybrid maps. This claim is verified experimentally by testing the performance of a hybrid map with and without synergy.</p>

Autonomous robots typically rely on internal representations of the environment, or maps, to plan and execute their tasks. Several types of maps have been proposed in the literature, and there is general consensus that different types have different advantages and limitations, and that each type is more suited to certain tasks and less to others. Because of these reasons, it is becoming common wisdom in the field of mobile robotics to use hybrid maps that integrate several representations, usually of different types. Hybrid maps provide scalability and multiple views, allowing for instance to combine robot-centered and human-centered representations. There is, however, little understanding of the general principles that can be used to combine different maps into a hybrid one, and to make it something more than the sum of its parts. There is no systematic analysis of the different ways in which different maps can be combined, and how they can be made to cooperate. This makes it difficult to evaluate and compare different systems, and precludes us from getting a clear understanding of how a hybrid map can be designed or improved. The investigation presented in this thesis aims to contribute to fill this foundational gap, and to get a clearer understanding of the nature of hybrid maps. To help in this investigation, we develop two tools: The first one is a conceptual tool, an analytical framework in which the main ingredients of a hybrid map are described; the second one is an empirical tool, a new hybrid map that allows us to experimentally verify our claims and hypotheses. While these tools are themselves important contributions of this thesis, our investigation has resulted in the following additional outcomes: • A set of concepts that allow us to better understand the structure and operation of hybrid maps, and that help us to design them, compare them, identify their problems, and possibly improve them; • The identification of the notion of synergy as the fundamental way in which component maps inside a hybrid map cooperate. To assess the significance of these outcomes, we make and validate the following claims: 1. Our framework allows us to classify and describe existing maps in a uniform way. This claim is validated constructively by making a thorough classification of the hybrid maps reported in the literature. 2. Our framework also allows us to enhance an existing hybrid map by identifying spots for improvement. This claim is verified experimentally by modifying an existing map and evaluating its performance against the original one. 3. The notion of synergy plays an important role in hybrid maps. This claim is verified experimentally by testing the performance of a hybrid map with and without synergy.

Made available in DSpace on 2014-12-17T14:54:56Z (GMT). No. of bitstreams: 1 AndreMS_TESE_1-100.pdf: 5113772 bytes, checksum: 19386f80f787c926c4fb29b85bac4ecf (MD5) Previous issue date: 2011-02-11 / The goal of this work is to propose a SLAM (Simultaneous Localization and Mapping) solution based on Extended Kalman Filter (EKF) in order to make possible a robot navigates along the environment using information from odometry and pre-existing lines on the floor. Initially, a segmentation step is necessary to classify parts of the image in floor or non floor . Then the image processing identifies floor lines and the parameters of these lines are mapped to world using a homography matrix. Finally, the identified lines are used in SLAM as landmarks in order to build a feature map. In parallel, using the corrected robot pose, the uncertainty about the pose and also the part non floor of the image, it is possible to build an occupancy grid map and generate a metric map with the obstacle s description. A greater autonomy for the robot is attained by using the two types of obtained map (the metric map and the features map). Thus, it is possible to run path planning tasks in parallel with localization and mapping. Practical results are presented to validate the proposal / O objetivo desta tese ? apresentar uma t?cnica de SLAM (Localiza??o e Mapeamento Simult?neos) adequada para ambientes planos com linhas presentes no ch?o, de modo a permitir que o rob? navegue no ambiente fundindo informa??es de odometria e de vis?o monocular. Inicialmente, ? feita uma etapa de segmenta??o para classificar as partes da imagem em ch?o e n?o-ch?o . Em seguida, o processadomento de imagem identifica linhas na parte ch?o e os par?metros dessas linhas s?o mapeados para o mundo, usando uma matriz de homografia. Finalmente, as linhas identificadas s?o usadas como marcos no SLAM, para construir um mapa de caracter?sticas. Em paralelo, a pose corrigida do rob?, a incerteza em rela??o ? pose e a parte n?och?o da imagem s?o usadas para construir uma grade de ocupa??o, gerando um mapa m?trico com descri??o dos obst?culos. A utiliza??o simult?nea dos dois tipos de mapa obtidos (m?trico em grade e de caracter?sticas) d? maior autonomia ao rob?, permitindo acrescentar tarefas de planejamento em simult?neo com a localiza??o e mapeamento. Resultados pr?ticos s?o apresentados para validar a proposta