Drones have been widely used in many areas of our daily lives. It relieves
people of the burden of holding a controller all the time and makes drone
control easier to use for people with disabilities or occupied hands. However,
the control of aerial robots is more complicated compared to normal robots due
to factors such as uncontrollable height. Therefore, it is crucial to develop
an intelligent UAV that has the ability to talk to humans and follow natural
language commands. In this report, we present an aerial navigation task for the
2023 ICCV Conversation History. Based on the AVDN dataset containing more than
3k recorded navigation trajectories and asynchronous human-robot conversations,
we propose an effective method of fusion training of Human Attention Aided
Transformer model (HAA-Transformer) and Human Attention Aided LSTM (HAA-LSTM)
model, which achieves the prediction of the navigation routing points and human
attention. The method not only achieves high SR and SPL metrics, but also shows
a 7% improvement in GP metrics compared to the baseline model.

发展能够与人类交流并遵循自然语言命令的智能无人机，该研究提出了一种基于 AVDN 数据集的高效融合训练方法，使用 HAA-Transformer 模型和 HAA-LSTM 模型，对导航路由点和人的注意力进行预测，并在 SR、SPL 指标上取得高效果，相比基线模型，GP 指标有 7% 的改进。