Due to the inability to receive signals from the Global Navigation Satellite
System (GNSS) in extreme conditions, achieving accurate and robust navigation
for Unmanned Aerial Vehicles (UAVs) is a challenging task. Recently emerged,
vision-based navigation has been a promising and feasible alternative to
GNSS-based navigation. However, existing vision-based techniques are inadequate
in addressing flight deviation caused by environmental disturbances and
inaccurate position predictions in practical settings. In this paper, we
present a novel angle robustness navigation paradigm to deal with flight
deviation in point-to-point navigation tasks. Additionally, we propose a model
that includes the Adaptive Feature Enhance Module, Cross-knowledge
Attention-guided Module and Robust Task-oriented Head Module to accurately
predict direction angles for high-precision navigation. To evaluate the
vision-based navigation methods, we collect a new dataset termed as UAV_AR368.
Furthermore, we design the Simulation Flight Testing Instrument (SFTI) using
Google Earth to simulate different flight environments, thereby reducing the
expenses associated with real flight testing. Experiment results demonstrate
that the proposed model outperforms the state-of-the-art by achieving
improvements of 26.0% and 45.6% in the success rate of arrival under ideal and
disturbed circumstances, respectively.

通过采用新颖的角度稳健导航范式和模型，结合自适应特征增强模块、跨领域注意力引导模块和鲁棒任务导向头模块，实现高精度导航的方向角预测，并经过实验证明所提出的模型在理想和干扰环境下的到达成功率分别提高了 26.0% 和 45.6%，从而解决了基于视觉的导航方法的局限性。