Robust Six Degrees of Freedom Estimation for IIoT Based on Multibranch Network

In diverse applications of the industrial Internet of Things (IIoT), the six degrees of freedom (6-DoF) information is essential, which determines the attitude and position of a 3-D object. Nevertheless, due to the complexity and variability of the scenarios, higher requirements are imposed on the 6-DoF estimation. Among them, occlusion is one of the knottiest problems, which causes significant performance degradation and needs to be solved urgently. Therefore, in this article, we propose a completely new and universal multibranch network (MBN) for industrial applications. Our method is based on monocular vision system and convolutional neural network (CNN) framework. First and foremost, it reduces occlusion interference by focusing on the physical area characteristics of the image. Compared with the traditional CNN-based method, it owns higher accuracy and lower estimation error under occlusion. Second, we propose five algorithms to process the predictions of the independent branches, further effectively improving performance. Third, we optimize the marker to solve the inequality problem in attitude angle estimation. Furthermore, we design and conduct a series of experiments, and the experimental results sufficiently prove the superiority of MBN.