Abstract: To realize universal, fast and accurate grasping of 6-DOF (degree of freedom) parts by an industrial robot, a 3D grasping method based on the guidance of monocular vision is proposed. Firstly, the similarity evaluation function between an image and a matching model is established by the Chamfer distance matching algorithm, in which the image is delimited according to direction angles. A genetic algorithm optimized locally by the hill climbing algorithm is applied to searching for the best matching result. Then, an offline 3D model library is established by CAD (computer aided design) model, and the matching algorithm is expanded to the spatial 6-DOF pose measurement of complex-structure parts. Finally, the grasping information is obtained by matrix transformations among all coordinates and the system calibration, so as to realize the 3D grasping of parts. The experiment results show that the optimized pose measurement algorithm improves the speed and accuracy of the matching process. With the proposed measurement algorithm, the position error within 2 mm and rotation error within 2° are achieved in the robotic 3D grasping experiments. So the measurement algorithm can be applied to the part grasping of industrial intelligent robots.