无人机在大型户外环境中的视觉定位方法
Chenhao Zhao1, Dewei Wu2, Jing He2
1Graduate School, Air Force Engineering University, Xi'an 710077, China.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了无人机的层次视觉定位系统,提高了准确性和速度. 这种新的方法提高了地标匹配,并将检索时间缩短了三分之二,达到10.8m的平均定位误差.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 人工智能的人工智能
背景情况:
- 基于结构的视觉定位对于无人机 (UAV) 操作至关重要.
- 现有的方法与照明和季节性变化作斗争,导致不可靠的本地化和缓慢的性能与大型数据库.
研究的目的:
- 为无人机开发一种新的层次视觉定位方法.
- 在具有挑战性的环境条件下提高视觉定位的效率和准确性.
主要方法:
- 一个认知地图是使用大脑启发的机制和地标构建的,用于高效的图像检索.
- 图形神经网络学习特征点关系,结合语义信心以提高匹配精度.
- 使用PNP解决器进行姿势计算,通过分析基于地标的匹配结果来消除不匹配.
主要成果:
- 拟议的方法显著减少了三分之二的图像检索时间.
- 在模拟数据集中实现了平均定位误差10.8米.
- 匹配算法在特定具有挑战性的场景中表现出卓越的性能.
结论:
- 层次视觉定位方法为无人机导航提供了强大而高效的解决方案.
- 认知地图和图形神经网络的整合提高了定位的准确性和速度.
- 这种方法解决了动态环境中传统视觉定位系统的关键局限性.
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