Research On Object Pose Estimation Method Based On Feature Fusion

With the development of intelligent manufacturing industry,many tasks in the industrial field needs the assistance of intelligent robots.And pose estimation plays a key role in the interaction between robots and environment.The study of pose estimation related methods has a certain practical significance.In recent years,pose estimation technology develops rapidly.However,the issue of texture-less object and occlusion between objects in pose estimation has not been well addressed.At present,many research methods use RGB images to obtain key information to realize pose estimation,and do not consider the impact of illumination factors on RGB image quality.RGB-D image provides additional depth information based on RGB information,which is conducive to high-precision pose estimation.In this paper,weak texture objects and occlusion objects are studied.The main research contents and innovations are as follows:(1)Aiming at the issue that the existing dense fusion methods ignore the geometric features between the local points of the point cloud,results in insufficient geometric feature extraction.An object pose estimation method based on feature fusion is proposed.First,the color features and point cloud features of the object are extracted from the RGB-D image.Second,fine local geometric features are extracted from the point cloud in the region through point set abstraction,and extended to a larger local region to obtain different levels of local geometric features and global geometric features of the object.Finally,the color features and geometric features of the object are fused.The initial pose is output by training the neural network.Experiments on LineMOD dataset and YCB-Video dataset show that compared with the comparison method,the average pose estimation accuracy of LineMOD dataset is improved by 0.5%-31.5%,and that of YCB-Video dataset is improved by 0.5%-5.6%.(2)Aiming at the issue that the two branches of feature extraction are carried out independently in the encoder-decoder feature fusion method,which limits the ability of feature extraction.Pose estimation method based on improved encoder-decoder feature fusion is proposed.First,in the encoding layer and decoding layer,the convolution layer is used to extract the object color feature.For the point cloud branch,the attention feature of the point cloud in the neighborhood is extracted as the local geometric feature by constructing the K-adjacent directed graph.Second,the two features are fused between each coding layer and decoding layer.Finally,the pose estimation is completed.Experiments on LineMOD dataset and Occlusion LineMOD dataset show that compared with the comparison method,the average pose estimation accuracy of LineMOD dataset is improved by 2.2%-14.1%,and the average pose estimation accuracy of Occlusion LineMOD dataset is improved by 5.6%21.5%.The proposed method has excellent pose estimation accuracy for texture-less and occluded objects.It has certain theoretical significance and application value for robot intelligence.