无人机地理定位数据集和基于交叉视图匹配的方法
Yuwen Yao1, Cheng Sun1, Tao Wang1
1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了用于无人机导航的VDUAV数据集和虚拟现实定位方法 (VRLM). 该VRLM模型提高了交叉视图地理位置的准确性,提高了无人机在充满挑战的环境中的稳定性.
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:
- 无人机依赖全球导航卫星系统 (GNSS) 进行稳定的飞行.
- 在复杂的环境中,GNSS信号不可靠,导致飞行不稳定.
- 交叉视图机器学习为强大的无人机本地化提供了潜力.
研究的目的:
- 引入一个新的数据集 (VDUAV) 用于交叉视图无人机定位.
- 开发一种新的网络架构 (VRLM),以改善地理位置.
- 解决现有数据集和本地化方法的局限性.
主要方法:
- 使用数字双胞胎平台和虚拟现实映射创建VDUAV数据集.
- 开发虚拟现实本地化方法 (VRLM) 与FocalNet骨干.
- 从无人机和卫星图像中提取特征,使用单独的分支和多尺度特征融合 (SCFF模块).
主要成果:
- VDUAV数据集显著降低了数据集生产成本.
- 在VDUAV数据集上,VRLM模型实现了83.35%的准确性 (MA@20) 和74.13%的精度 (RDS).
- 与FPI基线相比,VRLM表现优越.
结论:
- VDUAV数据集和VRLM网络为交叉视图无人机定位提供了一个强大的解决方案.
- 拟议的方法可以提高无人机在无GNSS环境中的导航稳定性.
- 这项工作为跨视图地理位置研究和应用开辟了新的途径.
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