使用RWA-YOLO的强大且具有成本效益的基于视觉的室内无人机定位
Feifei Wang1, Kun Sun1, Yuanqing Wang1
1School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了强大的波形感知YOLO (RWA-YOLO),这是一个用于精确室内无人机定位的视觉系统. 它在低光下达到厘米级的精度,优于其他基于视觉的方法.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 的室内定位在GPS被拒绝的区域很困难,特别是对于小物体和低光条件.
- 现有的方法通常在具有挑战性的室内环境中难以获得准确性和稳定性.
研究的目的:
- 为无人机在没有GPS的室内环境中开发一个强大的基于视觉的定位框架.
- 增强小物体检测和多尺度特征表示,以提高定位精度.
主要方法:
- 拟议的强大的波形感知YOLO (RWA-YOLO) 检测框架.
- 集成了一个波束意识的注意力融合模块和双重多路径聚合.
- 利用无人机上的LED用于低光视觉感知.
- 采用多视图几何三角测量,用于没有外部信标的3D位置估计.
主要成果:
- 实现了厘米级定位精度 (RMSE:9.9毫米,第95百分位:13.5毫米).
- 在低光和动态飞行条件下表现出强大的性能.
- 实时性能,更新速率大约为25 FPS.
- 性能优于最先进的基于视觉的方法,与混合系统可比.
结论:
- RWA-YOLO提供有效,稳固和实时的室内无人机导航.
- 该系统提供厘米级准确度,适合实时控制循环.
- 在真实室内环境中对静态和动态飞行条件进行验证.
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