  This paper proposes a visual-servoing method dedicated to grasping of
daily-life objects. In order to obtain an affordable solution, we use a
low-accurate robotic arm. Our method corrects errors by using an RGB-D sensor.
It is based on SURF invariant features which allows us to perform object
recognition at a high frame rate. We define regions of interest based on depth
segmentation, and we use them to speed-up the recognition and to improve
reliability. The system has been tested on a real-world scenario. In spite of
the lack of accuracy of all the components and the uncontrolled environment, it
grasps objects successfully on more than 95 percents of the trials.
