FE-GUT: 结合扩展卡尔曼滤波的因子图优化方法用于紧耦合GNSS/UWB融合
FE-GUT: Factor Graph Optimization hybrid with Extended Kalman Filter for tightly coupled GNSS/UWB Integration
- Tsinghua University（清华大学）
- Beijing Institute of Technology（北京理工大学）
- NORINCO group（中国兵器工业集团）
- Graph Optimization Inc.（图优化公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对低成本UWB与GNSS紧耦合融合中的时间同步问题，提出结合FGO与EKF的FE-GUT架构，通过FGO精确估计时间偏移、EKF完成初始化与补偿，显著提升定位与时间偏移估计精度。
AI中文摘要：
随着消费电子市场的发展，精准定位与导航信息的重要性日益凸显。由于全球导航卫星系统（Global Navigation Satellite System, GNSS）存在易受干扰等缺陷，将GNSS与其他备选传感器融合是克服基于GNSS的定位系统性能局限的有效途径。超宽带（Ultra-Wideband, UWB）可用于增强GNSS，构建融合定位系统。然而，多数低成本UWB设备缺少硬件级时间同步功能，因此在紧耦合GNSS/UWB融合中需要对时间偏移进行估计与补偿。得益于概率图模型的灵活性，时间偏移可在连续模型离散化过程中被建模为恒定不变量。本研究提出一种新型架构FE-GUT，即将因子图优化（Factor Graph Optimization, FGO）与扩展卡尔曼滤波（Extended Kalman Filter, EKF）结合，用于支持在线时间校准的紧耦合GNSS/UWB融合。其中FGO用于精确估计时间偏移，EKF为新因子提供初始化并执行时间偏移补偿。仿真实验验证了FE-GUT的融合定位性能。在四轮机器人场景下的结果表明，与EKF相比，FE-GUT可将水平和垂直定位精度分别提升58.59%和34.80%，同时时间偏移估计精度提升76.80%。所有源代码与数据集可通过https://github.com/zhaoqj23/FE-GUT/获取。
英文摘要：
Precise positioning and navigation information has been increasingly important with the development of the consumer electronics market. Due to some deficits of Global Navigation Satellite System (GNSS), such as susceptible to interferences, integrating of GNSS with additional alternative sensors is a promising approach to overcome the performance limitations of GNSS-based localization systems. Ultra-Wideband (UWB) can be used to enhance GNSS in constructing an integrated localization system. However, most low-cost UWB devices lack a hardware-level time synchronization feature, which necessitates the estimation and compensation of the time-offset in the tightly coupled GNSS/UWB integration. Given the flexibility of probabilistic graphical models, the time-offset can be modeled as an invariant constant in the discretization of the continuous model. This work proposes a novel architecture in which Factor Graph Optimization (FGO) is hybrid with Extend Kalman Filter (EKF) for tightly coupled GNSS/UWB integration with online Temporal calibration (FE-GUT). FGO is utilized to precisely estimate the time-offset, while EKF provides initailization for the new factors and performs time-offset compensation. Simulation-based experiments validate the integrated localization performance of FE-GUT. In a four-wheeled robot scenario, the results demonstrate that, compared to EKF, FE-GUT can improve horizontal and vertical localization accuracy by 58.59\% and 34.80\%, respectively, while the time-offset estimation accuracy is improved by 76.80\%. All the source codes and datasets can be gotten via https://github.com/zhaoqj23/FE-GUT/.
