Robot Depth Estimation Inspired by Fixational Movements

Abstract

Distance estimation is a challenge for robots, human beings, and other animals in their adaptation to changing environments. Different approaches have been proposed to tackle this problem based on classical vision algorithms or, more recently, deep learning. We present a novel approach inspired by mechanisms involved in fixational movements to estimate a depth image with a monocular camera. An algorithm based on microsaccades and head movements during visual fixation is presented. It combines the images generated by these micro-movements with the ego-motion signal, to compute the depth map. Systematic experiments using the Baxter robot in the Gazebo/ROS simulator are described to test the approach in two different scenarios and evaluate the influence of its parameters and its robustness in the presence of noise.