飞机的视觉导航算法在否定环境中融合神经网络
Yang Gao1, Yue Wang1, Lingyun Tian1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
这项研究介绍了飞机的轻量级视觉导航算法,提高了无卫星环境中的准确性. 神经网络融合的方法提高了飞机边缘计算性能和稳定性.
科学领域:
- 航空航天工程 航空航天工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 飞机边缘计算平台在处理能力方面面临限制,尤其是在没有卫星的环境中.
- 复杂的操作场景需要强大的导航解决方案,独立于外部信号.
研究的目的:
- 为飞机边缘计算开发一种轻量级的视觉导航算法.
- 在无卫星和复杂环境中提高导航准确性和稳定性.
主要方法:
- 神经网络与视觉导航技术的融合.
- 利用对象检测用于动态对象标签.
- 实现动态特征点消除以改善特征提取.
主要成果:
- 拟议的算法显著提高了导航准确性.
- 与单眼ORB-SLAM2等现有方法相比,证明了高强度.
- 在公开数据集 (TUM) 和实体飞行实验上均得到验证.
结论:
- 开发的算法有效地解决了飞机边缘计算中的计算限制.
- 在具有挑战性的操作条件下实现卓越的导航性能和稳定性.
- 适合在没有卫星连接的场景中实时应用.
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