一个室内无人机定位框架与ESKF紧密合融合和多时代UWB异常排斥框架
Jianmin Zhao1, Zhongliang Deng1, Enwen Hu1
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了无人机室内定位的新融合框架,通过整合惯性测量单元 (IMU),超宽带 (UWB) 和视觉惯性计时 (VIO) 数据来提高准确性和稳定性,同时拒绝错误.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 导航系统 导航系统
- 传感器融合式传感器
背景情况:
- 无人驾驶飞行器 (UAV) 需要可靠的室内定位来执行关键任务,面临全球导航卫星系统 (GNSS) 不可用性和传感器限制等挑战.
- 惯性测量单元 (IMU) 漂移,超宽带 (UWB) 非视线 (NLOS) 偏差和视觉惯性计量 (VIO) 功能依赖性阻碍了准确的室内导航.
- 现有的本地化方法在错误积累和环境遮蔽方面扎,需要先进的融合技术.
研究的目的:
- 开发一个紧密合的传感器融合框架,以实现强大而准确的室内无人机定位.
- 为了解决挑战性室内环境中的单个传感器 (IMU,UWB,VIO) 的局限性.
- 提高无人机导航系统的稳定性和可靠性.
主要方法:
- 提出了一个紧密结合的错误状态卡尔曼波器 (ESKF) 框架,集成IMU,UWB范围,VIO相对姿势和TFmini高度.
- 在更新步骤中实现了一个IMU运动模型用于预测和传感器数据融合.
- 开发了一种VIO受约束的多时代异常值排斥方法,以减轻UWB NLOS错误和传感器测量异常值.
主要成果:
- 与现有的算法相比,拟议的融合框架在复杂的室内环境中展示了优越的定位精度和稳定性.
- 有效地抑制了超宽带 (UWB) 非视线 (NLOS) 错误,并减轻了惯性测量单元 (IMU) 和视觉惯性计量 (VIO) 漂移.
- 在地下停车场的现实平台上进行验证,证实了实际应用.
结论:
- 紧密结合的ESKF框架通过融合多个传感器模式,显著提高了室内无人机本地化性能.
- 开发的异常值拒绝方法可稳定处理具有挑战性的UWB测量,提高整体系统可靠性.
- 这种方法为在没有GNSS的室内环境中准确和稳定的无人机导航提供了有希望的解决方案.
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