室内行人定位方法基于超宽带与图形卷积网络和视觉融合
Huizhen Mu1, Chao Yu1,2,3, Shuna Jiang1
1Engineering Center of SHMEC for Space Information and GNSS, East China Normal University, Shanghai 200241, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了一种结合超宽带 (UWB) 和视觉定位的新型室内定位方法. 综合方法显著提高了行人定位的准确性,优于现有的单一技术方法.
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
- 信号处理 信号处理
背景情况:
- 室内定位系统面临信号干扰和视觉扭曲等挑战,限制了准确性.
- 现有的方法,如超宽带 (UWB) 和视觉定位,在精度和连续性方面存在局限性.
- 准确的室内定位对于机器人,增强现实和导航等应用至关重要.
研究的目的:
- 开发和验证一种新的混合室内定位方法,集成UWB和视觉定位.
- 通过利用先进的机器学习技术,提高室内定位的准确性和连续性.
- 在复杂的室内环境中提高行人定位准确度.
主要方法:
- 超宽带 (UWB) 定位模块使用图形卷积网络 (GCN) 进行特征集成.
- 视觉定位模块使用双向门式循环单元 (Bi-GRU) 网络进行残余补偿.
- 使用颗粒过器 (PF) 融合UWB和视觉定位坐标,以提高准确性.
主要成果:
- 与Chan-Taylor算法相比,单个UWB定位精度提高了55% (误差<0.72米).
- 视觉定位准确度达到了0.42米,比张宇算法提高了71%.
- 合定位在0.24米以内实现了80%的准确性,最大误差为0.66米,相比单个UWB和视觉方法分别提高了56%和52%.
结论:
- 拟议的混合UWB和视觉定位方法显著提高了室内定位的准确性.
- 整合GCN用于UWB和Bi-GRU用于视觉定位,有效地解决了单个方法的局限性.
- 基于颗粒过器的聚变策略提供了强大而准确的室内行人定位.
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