用于无人机地理位置的图像匹配:经典和深度学习方法
Fatih Baykal1, Mehmet İrfan Gedik2, Constantino Carlos Reyes-Aldasoro3
1Department of Unmanned Aerial Vehicle Technology and Operator, Vocational School of Higher Education, OSTIM Technical University, 06374 Ankara, Türkiye.
Journal of imaging
|November 26, 2025
概括
无人机现在可以在没有全球导航卫星系统 (GNSS) 的情况下使用基于图像的地理定位系统进行导航. 这种方法将空中图像与卫星地图相匹配,即使在GNSS信号失效时,也可确保可靠的定位.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 地理空间智能是什么?
背景情况:
- 无人驾驶飞行器 (UAV) 在导航方面严重依赖全球导航卫星系统 (GNSS).
- GNSS信号容易受到干扰和伪造,造成重大安全风险.
- 这一漏洞影响军事行动和关键的民用任务,需要可靠的定位.
研究的目的:
- 开发基于图像的地理定位系统,以消除无人机对GNSS的依赖.
- 为了在GNSS信号受损或不可用的环境中实现可靠的无人机定位.
- 加强无人机的安全和运营能力.
主要方法:
- 通过将空中图像与地理参考卫星图像相匹配,估计无人机的地理位置.
- 利用常见的视觉特征和基于同谱矩阵的方法进行图像匹配.
- 将传统的特征检测算法 (SIFT,AKAZE,多模板匹配) 与深度学习方法 (SuperPoint,SuperGlue,LoFTR) 的比较.
主要成果:
- 通过图像处理,在无人机位置和地理坐标之间建立了强大的关系.
- 证明可靠的定位能力,即使GNSS信号无法使用.
- 基于深度学习的方法显示成功匹配特征,特别有效在高海拔地区.
结论:
- 基于图像的地理定位系统为无人机导航提供了一个可行的GNSS替代方案.
- 开发的系统增强了无人机的安全性和对GNSS威胁的运营弹性.
- 先进的深度学习技术显示出高空无人机定位使用视觉数据的前景.
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