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使用Wi-Fi RSS,嵌入式惯性传感器和CCTV图像进行混合室内定位的多模式数据集
Bawar Abid Abdalla1, Halgurd S Maghdid2, Azhin T Sabir1
1Department of Software Engineering, Faculty of Engineering, Koya University, Koya 44023, Kurdistan Region - F.R. Iraq.
Data in brief
|January 8, 2026
概括
这项研究介绍了混合室内定位的多式数据集,将Wi-Fi信号,惯性传感器数据和摄像头图像融合在一起,以克服设备异质性挑战. 这一数据集可以在没有全球导航卫星系统 (GNSS) 的情况下开发强大的室内定位系统.
科学领域:
- 机器人和自动化机器人与自动化
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 全球导航卫星系统 (GNSS) 对室内定位无效.
- 现有的室内定位方法由于设备异质性而面临挑战,影响准确性和稳定性.
- 需要全面的数据集来开发和测试混合室内定位技术.
研究的目的:
- 为混合室内本地化研究提供结构化的多式联运数据集.
- 促进开发传感器融合方法,结合Wi-Fi RSSI,惯性传感器数据和视觉信息.
- 创建一个测试设施,用于评估强大的室内定位系统.
主要方法:
- 在室内大学走廊使用Wi-Fi接入点,智能手机惯性传感器 (加速计,陀螺仪,磁力计) 和CCTV摄像头获取数据.
- 在1米分辨率网格上采样数据,每点进行10次重复,以提高可靠性.
- 在初步试验中,利用YOLOv8进行物体检测 (人,门),并使用卡尔曼和颗粒过器进行传感器融合.
主要成果:
- 开发了一个多式联网数据集,集成Wi-Fi RSSI,惯性传感器读数和视觉数据.
- 使用加权的3个最近邻居 (W3KNN) 和传感器融合技术,证明了初步定位的准确性.
- 该数据集支持信号波动建模,视觉辅助定位和基于机器学习的轨迹估计方面的先进研究.
结论:
- 提出的多式联络数据集对于推进混合室内定位研究具有价值.
- 该数据集解决了设备异质性的挑战,这对于实际的室内定位应用至关重要.
- 进一步的研究可以利用这个数据集来开发更准确,更强大的室内定位系统.
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