# Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives

arXiv: https://arxiv.org/abs/1509.06939 · HTML: https://mlsanity.com/paper/1509.06939/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2015.

## Built on by (most cited first, 4 of 4)

- [Wandering around: A bioinspired approach to visual attention through object motion sensitivity](https://mlsanity.com/paper/2502.06747/) (2025)
- [Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot](https://mlsanity.com/paper/2206.13462/) (2022)
- [Utilization of Non-verbal Behaviour and Social Gaze in Classroom Human-Robot Interaction Communications](https://mlsanity.com/paper/2312.06825/) (2023)
- [Event-based Selective Attention for Multi-resolution Fast Region of Interest (ROI) Detection](https://mlsanity.com/paper/2609.17134/) (2026)

## Abstract (arXiv)

The importance of depth perception in the interactions that humans have within their nearby space is a well established fact. Consequently, it is also well known that the possibility of exploiting good stereo information would ease and, in many cases, enable, a large variety of attentional and interactive behaviors on humanoid robotic platforms. However, the difficulty of computing real-time and robust binocular disparity maps from moving stereo cameras often prevents from relying on this kind of cue to visually guide robots' attention and actions in real-world scenarios. The contribution of this paper is two-fold: first, we show that the Efficient Large-scale Stereo Matching algorithm (ELAS) by A. Geiger et al. 2010 for computation of the disparity map is well suited to be used on a humanoid robotic platform as the iCub robot; second, we show how, provided with a fast and reliable stereo system, implementing relatively challenging visual behaviors in natural settings can require much less effort. As a case of 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. Indeed this example paves the way to a variety of other similar applications.