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.

翻译：在极端条件下，由于无法接收全球导航卫星系统（GNSS）信号，实现无人机的精确鲁棒导航是一项具有挑战性的任务。近年来，基于视觉的导航已成为一种有前景且可行的GNSS导航替代方案。然而，现有视觉技术难以应对实际场景中环境扰动导致的飞行偏差以及位置预测不准确的问题。本文提出了一种新颖的角度鲁棒性导航范式，用于解决点对点导航任务中的飞行偏差问题。此外，我们构建了一个包含自适应特征增强模块、跨知识注意力引导模块和鲁棒任务导向头模块的模型，以精确预测方向角，实现高精度导航。为评估基于视觉的导航方法，我们采集了新数据集UAV_AR368。同时，利用谷歌地球设计了仿真飞行测试平台（SFTI），模拟不同飞行环境，从而降低实际飞行测试成本。实验结果表明，所提模型在理想环境和受扰环境下的到达成功率分别提升了26.0%和45.6%，优于现有最优方法。