Comparison of Smart Visual Attention Mechanisms for Humanoid Robots

Abstract

Cameras are one of the most relevant sensors in autonomous robots. One challenge with them is to manage the small field of view of regular cameras. A method of coping with this, similar to the attention systems in humans, is to use mobile cameras to cover all the robot surroundings and to perceive all the objects of interest to the robot tasks even if they do not lie in the same snapshot. A gaze control algorithm is then required that continuously selects where the camera should look. This paper presents three different covert attention mechanisms that have been designed and compared: one based on round-Robin sharing, another based on dynamic salience and one with fixed pattern camera movements. Several experiments have been performed with a humanoid robot in order to validate them and to give an objective comparison in the context of RoboCup, where the robots have several perceptive needs like localization and object tracking that must be satisfied and may not be fully compatible.