An improved 6D target pose estimation algorithm for feature extraction networks
In order to solve the problem of real-time and accurate pose estimation for weakly textured and occluded targets,a 6D target pose estimation algorithm based on an improved feature extraction network is proposed in the DenseFusion framework.Firstly,in the stage of image feature extraction,skip connections and attention mechanism modules are added to effectively fuse deep and shallow features,improving the richness and effectiveness of feature information;Secondly,in the point cloud feature extraction stage,PointNet is used for initial feature extraction of the point cloud,and then K-nearest neighbor method and global pooling are used to obtain richer point cloud feature information;Finally,the image features and point cloud features are densely fused for pose estimation and pose refinement.The experimental shows that our method outperforms DenseFusion on the LineMOD dataset and Occlusion LineMOD dataset,and the improved image feature extraction network and the improved point cloud feature extraction network can effectively improve the accuracy of pose estimation whether used alone or in combination.
万方数据
维普
