The existing three-dimensional object recognition and pose estimation methods cannot solve the scene of random bins well, especially for scenes with severe occlusion and clutter. Aiming at this problem, a point cloud matching and pose estimation algorithm based on point pair features is used in this paper. A series of improvements are made to obtain more ideal pose estimation results according to the characteristics of random bins in industrial environments, such as the adjustment of the normal direction consistency of the scene point clouds, the adjustment of the grab pose filtering strategy, and the adjustment of angular deviation caused by the rotation symmetry. In this paper, a series of experiments are carried out in the simulation environment and the real environment. Experimental results show that the adopted algorithm has good pose estimation effect in the scene of random bins.