To resolve the problem of generating grasping poses for unknown objects in an unstructured environment, a method based on point cloud sampling weight estimation is proposed to generate grasping pose. Firstly, the object point cloud information is spliced by moving camera, then the geometric characteristics of the object are utilized to improve grasp quality. In addition, the candidates pose can be evaluated by geometric constraints, while the force closure condition is used to quantify pose stability. Finally, in order to select the optimal pose to perform the grasping task, set reliability indicators based on pose stability, depth and angle. Experimental results show that the proposed method can efficiently generate a large number of stable grasping poses, and complete the grasping tasks of different unknown objects by the manipulator in the simulation environment effectively.