SaMfENet: Self-Attention Based Multi-Scale Feature Fusion Coding and Edge Information Constraint Network for 6D Pose Estimation

Accurate estimation of an object’s 6D pose is one of the crucial technologies for robotic manipulators. Especially when the lighting conditions changes or the object is occluded, resulting in the missing or the interference of the object information, which makes the accurate 6D pose estimation more challenging. To estimate the 6D pose of the object accurately, a self-attention-based multi-scale feature fusion coding and edge information constraint 6D pose estimation network is proposed, which can achieve accurate 6D pose estimation by employing RGB-D images. The proposed algorithm first introduces the edge reconstruction module into the pose estimation network, which improves the attention of the feature extraction network to the edge features. Furthermore, a self-attention multi-scale point cloud feature extraction module, i.e., MSPNet, is proposed to extract point cloud geometric features, which are reconstructed from depth maps. Finally, the clustering feature encoding module, i.e., SE-NetVLAD, is proposed to encode multi-modal dense feature sequences to construct more expressive global features. The proposed method is evaluated on the LineMOD and YCB-Video datasets, and the experimental results illustrate that the proposed method has an outstanding performance, which is close to the current state-of-the-art methods.