GNSS拒止场景下的角度鲁棒无人机导航
Angle Robustness Unmanned Aerial Vehicle Navigation in GNSS-Denied Scenarios
AI总结：
针对GNSS拒止环境下无人机导航的飞行偏差问题，提出角度鲁棒导航范式及含三个模块的模型，并构建数据集和模拟测试平台，在理想和扰动条件下到达成功率分别提升26.0%和45.6%。
摘要：
在极端条件下，由于无法接收全球导航卫星系统（GNSS）的信号，实现无人机（UAV）的精确且鲁棒的导航是一项具有挑战性的任务。近期出现的基于视觉的导航已成为基于GNSS导航的一种有前景且可行的替代方案。然而，现有的基于视觉的技术在解决实际环境中由环境扰动引起的飞行偏差和不准确的位置预测方面存在不足。本文提出了一种新颖的角度鲁棒导航范式，以处理点对点导航任务中的飞行偏差。此外，我们提出了一种模型，该模型包括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，以准确预测方向角度，实现高精度导航。为了评估基于视觉的导航方法，我们收集了一个名为UAV_AR368的新数据集。此外，我们使用谷歌地球设计了模拟飞行测试仪器（SFTI），以模拟不同的飞行环境，从而降低与真实飞行测试相关的费用。实验结果表明，所提出的模型在理想和受扰动情况下，到达成功率分别提高了26.0%和45.6%，优于现有最先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
