Hybrid Visual Servoing for Autonomous Robotic Laser Tattoo Removal Laser tattoo removal is a standard noninvasive method for removing color pigments on the skin. Increasing number of tattooed people who want to remove their tattoo has driven the medical laser market to develop new technologies for painless, scarfree and complete tattoo removal. However, manual use of such laser systems creates postoperative complications since they do not guarantee (i) protection on nontattooed skin from laser exposure, nor (ii) precise control of the laser focus during the operations for best performance. This paper introduces deTattoo, a robotic system to improve tattoo removal operations. A robotic arm is equipped with a RGBD camera and a visible laser, in eyeinend configuration. A hybrid visual servoing control is proposed to guarantee the correct pose of the laser with respect to the tattooed tissue while compensating body motions. 2D features tracked with a massspringdamper deformable mesh model are combined with the 3D reconstruction retrieved from a RGBD camera in order to build the control law. Several experiments were conducted to evaluate the performance of the system with a fixed or moving tattooed surface, at different inclinations. Results showed that the proposed framework is able to fulfil the laserbased tattoo removal requirements, providing high positioning accuracy (lt 1 mm) orientation (lt 0. 2circ) and body motion compensation.

RGBD Image Processing Algorithm for Target Recognition and Pose Estimation of Visual Servo System This paper studies the control performance of visual servoing system under the planar camera and RGBD cameras, the contribution of this paper is through rapid identification of target RGBD images and precise measurement of depth direction to strengthen the performance indicators of visual servoing system such as real time and accuracy, etc. Firstly, color images acquired by the RGBD camera are segmented based on optimized normalized cuts. Next, the gray scale is restored according to the histogram feature of the target image. Then, the obtained 2D graphics depth information and the enhanced gray image information are distort merged to complete the target pose estimation based on the Hausdorff distance, and the current image pose is matched with the target image pose. The end angle and the speed of the robot are calculated to complete a control cycle and the process is iterated until the servo task is completed. Finally, the performance index of this control system based on proposed algorithm is tested about accuracy, realtime under positionbased visual servoing system. The results demonstrate and validate that the RGBD image processing algorithm proposed in this paper has the performance in the above aspects of the visual servoing system.