Information transmission in underwater conditions cannot depend on various media, thus an Autonomous Underwater Vehicle (AUV) is imperative to assist divers with conducting underwater operations and information transmission. However, little work has been done to promote the interaction between AUV and divers. To help resolve this issue, this paper proposes a method to help the navigation guidance of AUV by predicting the divers’ headings. In this work, the visual method is chosen to estimate pose of divers since it can adapt to complex underwater environment conditions at a lower cost than other methods. The contour information is used to obtain the keypoints of the diver which are input into the Long-Short Term Memory Network to predict the headings. The dataset which redefines the skeleton of divers in our work is original and open for future researches. Compared with the original method, the accuracy of pose estimation on this dataset is 85\(\%\), an increase of 9\(\%\), and with a small error of diver’s heading prediction, which indicates that our method can obtain competitive prediction results.

水下条件下的信息传输不能依赖于各种媒体，因此自主水下航行器（AUV）对于协助潜水员进行水下作业和信息传输势在必行。然而，在促进 AUV 与潜水员之间的互动方面几乎没有做任何工作。为了帮助解决这个问题，本文提出了一种通过预测潜水员的航向来帮助AUV导航引导的方法。在这项工作中，选择视觉方法来估计潜水员的姿势，因为它可以以比其他方法更低的成本适应复杂的水下环境条件。轮廓信息用于获取潜水员的关键点，输入长短期记忆网络以预测航向。在我们的工作中重新定义潜水员骨架的数据集是原创的，对未来的研究开放。与原始方法相比，该数据集的姿态估计准确率为85\(\%\)，增加了 9 \(\%\)，并且潜水员的航向预测误差很小，这表明我们的方法可以获得有竞争力的预测结果。