  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.
