基于Wi-Fi和视觉的自适应卡尔曼波器融合定位
Shuxin Zhong1, Li Cheng1, Haiwen Yuan1
1College of Electrical Information, Wuhan Institute of Technology, Wuhan 430205, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种融合自适应卡尔曼波器 (FAKF),用于增强室内定位. 结合Wi-Fi和视觉数据可以显著减少错误,提高位置准确性和稳定性.
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
- 传感器融合式传感器
背景情况:
- 室内定位系统 (IPS) 需要提高准确性和稳定性.
- 当前的单传感器方法 (Wi-Fi,视觉) 在精度和稳定性方面存在局限性.
研究的目的:
- 提出一个新的Wi-Fi和基于视觉的融合自适应卡尔曼波器 (FAKF) 改进室内定位.
- 通过传感器融合,提高室内定位的准确性和稳定性.
主要方法:
- 使用随机森林算法与区域限制开发了增强的Wi-Fi定位.
- 集成的YOLOv7对象检测和深度SORT跟踪,提供稳定的视觉定位.
- 实现了一个自适应的卡尔曼波器,该波器融合Wi-Fi和视觉数据,根据实时测量余数动态调整参数.
主要成果:
- 与单个传感器方法相比,FAKF方法显示定位错误显著减少.
- 合并的传感器数据提供了对目标实际位置的更准确的反映.
- 实验验证证证实了拟议的融合方法的提高准确性和稳定性.
结论:
- 传感器融合,特别是使用FAKF方法,大大提高了室内定位性能.
- 卡尔曼过中的自适应参数调整对于优化合传感器数据至关重要.
- 拟议的方法为准确的实时室内定位提供了一个强大的解决方案.
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