一个语义关联因子图模型用于LiDAR辅助的室内多路径定位
Bingxun Liu1, Ke Han1, Zhongliang Deng1
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
这项研究引入了一种LiDAR辅助的方法,用于精确的室内定位,提高了32.1%的精度. 它有效地使用环境数据来纠正来自无线信号反射的错误,提高复杂空间的稳定性.
科学领域:
- 机器人技术和自主系统
- 无线通信和信号处理
- 地理学工程 工程地质学
背景情况:
- 全球导航卫星系统 (GNSS) 在室内无法使用.
- 像5G和超宽带 (UWB) 这样的无线信号用于室内定位.
- 复杂的室内结构的多路径效应会降低定位精度.
研究的目的:
- 开发一种LiDAR辅助的方法,用于多路径误差估计和高精度室内定位.
- 整合环境语义信息,以改善多路径校正.
- 为了提高复杂的室内环境中的定位稳定性.
主要方法:
- 使用LiDAR和无线信号的紧密结合的感知定位框架.
- 一个基于语义特征的神经网络,用于从LiDAR点云中检测反射表面.
- 一个统一的因子图模型,用于联合推断状态和反射器信息.
主要成果:
- 使用LiDAR精确提取反射器的几何参数.
- 动态区分和利用视线 (LOS) 和非视线 (NLOS) 路径.
- 与传统方法相比,根平均平方误差 (RMSE) 提高了32.1%.
结论:
- 拟议的方法有效地解决了复杂的室内环境中的多路径效应.
- 激光雷达辅助的语义信息显著提高了室内定位的准确性和稳定性.
- 为高精度定位在具有挑战性的室内环境中提供了可行的解决方案.
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