Enabling depth-driven visual attention on the iCub humanoid robot: Instructions for use and new perspectives

Abstract

Reliable depth perception eases and enables a large variety of attentional and interactive behaviors on humanoid robots. However, the use of depth in real-world scenarios is hindered by the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras. On the iCub humanoid robot, we recently adopted the Efficient Large-scale Stereo (ELAS) Matching algorithm (Geiger et al., 2010) for computation of the disparity map. In this technical report, we show that this algorithm allows reliable depth perception and experimental evidence that demonstrates that it can be used to solve challenging visual tasks in real-world indoor settings. As a case study, we consider the common situation where the robot is asked to focus the attention on one object close in the scene, showing how a simple but effective disparity-based segmentation solves the problem in this case. This example paves the way to a variety of other similar applications.