Reusable prediction network for target 6D attitude real-time estimation

This paper proposes a rigid body 6D attitude estimation method based on a reusable prediction network. Method: First, two independent feature encoders extract the feature information matrix from taking the RGB-D image at the current time and the target rendering model at a previous time. Second, The above two characteristic information matrices are fused and introduced into the multiplexing prediction network, and the prediction rotation matrix is obtained by decoupling the feature information fusion matrix. Finally, by decoupling the fused matrix, the rotation and translation matrices are obtained. Additionally, a channel weight allocation module in conjunction with a residual network is added to improve the accuracy of real-time attitude estimation. Conclusions: Training the reusable prediction network with the YCBInEOAT dataset, the network can converge faster. In addition, the mean distance values and symmetric mean distance values in the evaluation metrics are greater than those obtained using the se(3)-TrackNet method, with a 7.29% increase in mean distance values and a 13.78% increase in symmetric mean distance values.