基于Strapdown惯性导航系统/无人机场景匹配导航系统的综合导航方法
Yukun Wang1, Qiang Wang1, Zhonghu Hao1
1School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
本研究介绍了一种无人驾驶飞行器 (UAV) 的综合导航方法,使用带式惯性导航系统 (SINS) 和场景匹配导航系统 (SMNS). 在具有挑战性的环境中,SINS/SMNS方法显著提高了无人机定位精度.
科学领域:
- 机器人和自动化 机器人和自动化
- 导航系统 导航系统
- 计算机视觉 计算机视觉
背景情况:
- 无人驾驶飞行器 (UAV) 在全球导航卫星系统 (GNSS) 拒绝的环境中面临导航挑战.
- 不连续的异质图像匹配和区域错误降低了导航准确度.
- 实时性能对于无人机操作至关重要.
研究的目的:
- 为无人机开发一种综合导航方法,克服GNSS拒绝的环境限制.
- 提高无人机定位的准确性和可靠性.
- 为了增强实时导航能力.
主要方法:
- 使用红外图像设计了一种异质的图像匹配和定位方法.
- 开发了一个数学模型,用于集成的带式惯性导航系统 (SINS) /场景匹配导航系统 (SMNS).
- 使用卡尔曼波器 (KF) 来融合SINS和SMNS数据.
主要成果:
- 与独立场景匹配相比,集成的SINS/SMNS方法显示出更高的水平定位精度.
- 度准确度提高了52.34%,经度准确度提高了45.54%.
- 实现了高精度和可靠的导航定位.
结论:
- 拟议的集成SINS/SMNS导航方法有效地解决了在GNSS被拒绝的场景中无人机导航的挑战.
- 这种方法在定位准确性和可靠性方面提供了显著的改进.
- 该方法适用于实时无人机导航应用.
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