基于UDU的3D室内位置估计,因子化扩展卡尔曼波器结构使用信标距离和惯性测量单元数据
Tolga Bodrumlu1, Fikret Caliskan2
1Mechatronics Engineering Department, Istanbul Technical University, Istanbul 34025, Turkey.
Sensors (Basel, Switzerland)
|May 25, 2024
概括
这项研究提出了一个精确的室内定位算法,使用扩展的卡尔曼波器结合了超声波和惯性数据. 开发的系统达到厘米级的精度,优于传统方法.
科学领域:
- 机器人和自动化 机器人和自动化
- 传感器融合式传感器
- 导航系统 导航系统
背景情况:
- 全球定位系统 (GPS) 在户外有效,但室内定位需要更高效,可靠和具有成本效益的技术.
- 现有的室内定位方法包括基于Wi-Fi,蓝牙,红外,超声波,磁性和视觉标记器的系统.
- 需要先进的算法来提高室内环境中的准确性和可靠性.
研究的目的:
- 通过融合超声波传感器和惯性测量单元的数据来设计一个准确的室内位置估计算法.
- 使用扩展的卡尔曼波器 (EKF) 实现和评估该算法,并使用UDU分解.
- 为了实现室内定位应用的厘米级精度.
主要方法:
- 结合了来自超声波传感器 (Marvelmind Beacon) 的原始距离数据和来自惯性测量单元 (IMU) 的加速数据.
- 使用扩展卡尔曼波器 (EKF) 与UDU因子化进行传感器融合和位置估计.
- 采用递归最小平方 (RLS) 方法与三边化计算初始位置.
- 使用机器人操作系统 (ROS) 收集的数据,并在Pixhawk开发卡上实现.
主要成果:
- 开发的算法准确地以厘米精度估计了位置.
- 集成到嵌入式系统中的UDU-EKF结构与经典EKF相比,显示出更快的性能.
- 测试证实了使用各种固定和移动传感器配置的算法的有效性.
结论:
- 拟议的算法有效地融合了超声波和惯性数据,用于准确的室内定位.
- UDU-EKF方法为嵌入式室内导航系统提供了计算效率高和精确的解决方案.
- 该系统达到厘米级准确度,适用于需要高精度定位的应用.
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