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. 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.
Depth is a natural cue to be used when the robot's attention needs to be focused on close entities in its workspace. For example, consider a very common situation for a humanoid robotic platform, like the one where a human stands in front of the robot showing to it an object to be recognized or grasped. Both motion- and appearance-based approaches to focus the robot's attention on the object of interest would impose many constraints on this even simple Human-Robot Interaction (HRI) scenario. Indeed, color-based methods work under strict assumptions on the light conditions, kind of background (preferably a table or a wall) and generally fail in cluttered settings. Model-based methods, beyond being affected to some extent by the same limitations, need a model of the object to be known a-priori. Motion-based methods (see, e.g., @cite_12 ) work under the obvious assumption that the objects are moving, the speed of the object being often critical for the detection. Instead, when the robot is required to look at something we are showing to it, or which is located nearby, the most distinguishing feature is simply the fact that the object of interest is closer to the robot than the background.
@cite_12
2170549527
Visual motion is a simple yet powerful cue widely used by biological systems to improve their perception and adaptation to the environment. Examples of tasks that greatly benefit from the ability to detect movement are object segmentation, 3D scene reconstruction and control of attention. In computer vision several algorithms for computing visual motion and optic flow exist. However their application in robotics is not straightforward as in these platforms visual motion is often dominated by (self) motion produced by the movement of the robot (egomotion) making it difficult to disambiguate between motion induced by the scene dynamics or by the own actions of the robot. Independent motion detection is an active field in computer vision and robotics, however approaches in this area typically require that some models of both the environment and the robot visual system are available and are hardly suitable for real-time control. In this paper we describe the motionCUT, a derivation of the Lucas-Kanade optical flow algorithm that allows detecting moving objects, irrespectively of the egomotion produced by the robot. Our method is purely visual and does not require information other than the images coming from the cameras. As such it can be easily adapted to any robotic platform. The system was tested on a stereo tracking task on the iCub humanoid robot, demonstrating that the algorithm performs well and can easily execute in real-time.