Object pose estimation for RGB-D based on asymmetric geometric feature extraction fusion

6DoF (6 Degrees of Freedom, or 6DoF or 6D) pose estimation has practical applications in robot vision, 3D scene understanding and other fields. 6D pose estimation based on RGB-D images has an important role in robotic applications, however most current methods directly stitch together RGB and depth information, resulting in low accuracy of pose estimation. This paper presents a novel object 6 DoF pose estimation method based on RGB-D images, which improves on the insufficient accuracy of pose estimation. In this paper, we transform the depth map into point clouds, design an asymmetric geometric feature extraction network AGF to effectively extract the geometric information in point clouds, and combine with EfficentNet-B7 network to extract RGB features. In order to make full use of the cross-mode information, the primary and auxiliary feature interaction module MAF is proposed, which can realize the efficient fusion of RGB and point cloud features. Moreover, this paper introduces an adaptive feature contribution weight calculation module AWF, which is able to adaptively calculate the different contribution weights of the two modes to the pose estimation task, thus further improving the estimation accuracy. The experimental results show that the proposed method shows higher accuracy and robustness in the object 6 DoF pose estimation task.