Depth-Based Visual Servoing Using Low-Accurate Arm
  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. 
Ludovic Hofer, Michio Tanaka, Hakaru Tamukoh, Amir Ali Forough   Nassiraei, Takashi Morie
cs.RO