Model-based visual self-localization using geometry and graphs

In this paper, a geometric approach for global self-localization based on a world-model and active stereo vision is introduced. The method uses class specific object recognition algorithms to obtain the location of entities within the surroundings. The perceived entities in recognition trials are simultaneously filtered and fused to provide a robust set of class features. These classified perceptions which simultaneously satisfy geometric and topological constraints are employed for pruning purposes upon the world-model generating the location hypotheses set. Finally, the hypotheses are validated and disambiguated by applying visual recognition algorithms to selected entities of the world-model. The proposed approach has been successfully used with a humanoid robot.