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Although new techniques that enable robots with advanced cognitive capabilities are being developed, still few work is being devoted to a difficult problem in which all this techniques rely: Perception.
Indeed, being able to identify the location and what objects lie around constitute the foundations in which almost all high-level reasoning processes will build up.
In order to help reduce a bit this gap, this work addresses the problems of vision-based global localization and object recognition.
The first contributions presented are a new technique to construct signatures of places to be used as nodes of a topological map from constellations of features detected in panoramic images, and a homing method to travel between such nodes that does not rely in artificial landmarks. Both methods were tested in several datasets showing very good results.
General object recognition in mobile robots is of primary importance in order to enhance the representation of the world that robots will use for their reasoning processes.
Therefore, the next contributions of the thesis address this problem. After carefully reviewing recent Computer Vision literature on this topic, two state of the art object recognition methods were selected: The SIFT Object Recognition method and the Vocabulary Tree method.
After evaluating both methods under challenging conditions, focusing in issues relevant to mobile robotics, it was found that, although the SIFT method was more suited for mobile robotics, both had complementary properties.
To take advantage of this complementarity, the final contribution of this thesis is a Reinforcement Learning method to select online which object recognition method is best for an input image based on simple to compute image features. This method has been validated in a challenging object recognition experiment, even improving the performance of a human expert in some cases.