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Mapas auto-organizáveis de topologia variante no tempo para SLAM visual em espaço de aparências

Implement autonomous robotic agents able to navigate between two positions or explore unknown environments are old and important problems in areas such as robotics and artificial intelligence. A prerequisite to support the navigation and autonomous exploration activities is to provide the robotic agent ability to build maps of the around environment and be able to identify its position in the map. This is the simultaneous localization and mapping problem (SLAM) and can be mathematically modeled as a filtering problem. In the last 10 years were obtained great advances in this area with the development of various approaches to solving this problem. The most popular approaches use statistical filters, similar to the Kalman, and aim to build maps with metric scale, similar to those used in cartography. Regarding the sensory apparatus, the most common sensors type are range finders, it are, directly or indirectly, capture metric distance and the relationships of a given environment. Examples of sensors of this type are: Lidar, Sonar, Radar. This approach results, in general, in an expensive and heavy sensory apparatus, a high-powered data processing, high energy consumption and a complex and specific mathematical modeling for each type of mobile agent. It also does not appear to be a natural approach, since most living beings does not require metric knowledge need the ambiance this around to locate the environment and explore unknown environments. Thus, this thesis aims to provide a more natural approach to the SLAM problem using available resources most animals, such as vision and neighborly relations memorization. This thesis conducts a literature review of articles and scientific projects of the last 20 years related to the field of robotics and intelligent navigation and proposes a new approach for Visual SLAM problem using only monocular vision as input stimuli and self-organizing maps dynamic specialize in memorizing states trajectories.