Robust real-time 6D active visual localization for humanoid robots

David Israel Gonzalez-Aguirre, Michael Vollert, Tamim Asfour, Rüdiger Dillmann

Abstract: Overcoming the perceptual limitations of hu-manoid robots requires representations exploitable by highly integrable simulation, sensing, planning and acting components. Therefore, a novel active visual localization component for humanoid robots based on particle filtering in CAD environments is introduced. Specifically, two new components are presented: i) A vector-graphics prediction method employing hierarchical CAD environmental representations is presented. ii) A gaze attention method within the prediction-update cycle of the particle filter increases the available amount of visual features for localization while allowing adjustable task coupling. Finally, large and unobstructive ground-truth validation with the humanoid robot ARMAR-IIIb [1] in a made-for-humans environment shows the robustness, accuracy and performance of the proposed methods.