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Map building and localization are two important abilities that autonomous mobile robots must develop. This way, much research has been carried out on these topics, and researchers have proposed many approaches to address these problems. This work presents some possibilities to solve these problems robustly using global appearance descriptors. In this way, robots capture visual information from the environment and obtain, usually by means of a transformation, a global appearance description for each whole image. Using these descriptors, the robot is able to estimate its position in a previously built map, which is also composed of a set of global appearance descriptors. In this work, the global appearance descriptors performance at the localization task inside a known environment has been studied. In the assignment a map is already built and the goal is to evaluate the descriptors’ robustness to perform localization tasks when the environment visual appearance changes substantially. To achieve this objective, a comparative evaluation of several global appearance descriptors is carried out.

Evaluating the Robustness of Global Appearance Descriptors in a Visual Localization Task, under Changing Lighting Conditions