5,603 research outputs found
Markerless visual servoing on unknown objects for humanoid robot platforms
To precisely reach for an object with a humanoid robot, it is of central
importance to have good knowledge of both end-effector, object pose and shape.
In this work we propose a framework for markerless visual servoing on unknown
objects, which is divided in four main parts: I) a least-squares minimization
problem is formulated to find the volume of the object graspable by the robot's
hand using its stereo vision; II) a recursive Bayesian filtering technique,
based on Sequential Monte Carlo (SMC) filtering, estimates the 6D pose
(position and orientation) of the robot's end-effector without the use of
markers; III) a nonlinear constrained optimization problem is formulated to
compute the desired graspable pose about the object; IV) an image-based visual
servo control commands the robot's end-effector toward the desired pose. We
demonstrate effectiveness and robustness of our approach with extensive
experiments on the iCub humanoid robot platform, achieving real-time
computation, smooth trajectories and sub-pixel precisions
Enabling Depth-driven Visual Attention on the iCub Humanoid Robot: Instructions for Use and New Perspectives
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
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