基于场景匹配校正的视觉惯性口径测量中测量错误规则的方法
Haiqiao Liu1, Zichao Gong1, Jinxu Shen2
1The School of Electrical and Information, Hunan Institute of Engineering, Xiangtan 411104, China.
Micromachines
|November 27, 2024
概括
这项研究引入了一种新的方法,用于测量无人驾驶飞行器 (UAV) 中的视觉惯性测距误差,使用场景匹配校正. 拟议的VN+MEMS+SM模型在特定条件下提供了比MEMS+SM模型更好的准确性.
科学领域:
- 机器人技术和自主系统
- 导航和控制 导航和控制
- 计算机视觉 计算机视觉
背景情况:
- 无人驾驶飞行器 (UAV) 在集成导航准确性方面面临挑战.
- 现有的视觉惯性测距方法需要增强的误差测量技术.
研究的目的:
- 提出和验证一种用于测量无人机集成导航错误的新方法.
- 通过场景匹配校正来提高视觉惯性测距的准确性.
主要方法:
- 开发了用于视觉导航,微电子机械系统 (MEMS) 导航和场景匹配的单独模型.
- 构建了集成的导航错误测量模型:MEMS+SM和VN+MEMS+SM.
- 基于场景匹配精度,时间和MEMS精度进行实验评估模型性能.
主要成果:
- VN+MEMS+SM和MEMS+SM模型在场景匹配精度<10 m和时间<10 s时显示出类似的错误.
- 在10秒匹配时间内确定MEMS+SM模型中特定平均误差的关键场景匹配精度.
- 当MEMS精度为150,场景匹配精度为50米,时间超过135秒时,VN+MEMS+SM模型的表现优于MEMS+SM.
结论:
- 拟议的VN+MEMS+SM模型增强了无人机的综合导航错误测量.
- 场景匹配校正对于提高视觉惯性测距精度至关重要.
- 最佳性能取决于平衡场景匹配参数和传感器精度.
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