Attention-based environment perception in autonomous robotics
Antonio Chella, Irene Macaluso and Lorenzo Riano
The 10th Congress of the Italian Association for Artificial Intelligence - Special Track on AI and Robotics
Roma, Italy, September 10-13, 2007
Abstract
This paper describes a robotic architecture that uses visual attention mechanisms for autonomous navigation in unknown indoor environments. A foveation mechanism based on classical bottom-up gaze shifts allows the robot to autonomously select landmarks, defined as salient points in the camera images. Landmarks are memorized in a behavioral fashion, coupling sensing and acting to achieve a representation view and scale independent. Selected landmarks are stored in a topological map; during the navigation a top-down mechanism controls the attention system to achieve robot localization. Experiments and results show that our system is robust to noise and odometric errors, being at the same time adaptable to different environments and acting conditions.