FE-GUT: Factor Graph Optimization hybrid with Extended Kalman Filter for tightly coupled GNSS/UWB Integration
<|reference_start|>FE-GUT: Factor Graph Optimization hybrid with Extended Kalman Filter for tightly coupled GNSS/UWB Integration: 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/.<|reference_end|>
@article{zhao2024fe-gut:,
    title={FE-GUT: Factor Graph Optimization hybrid with Extended Kalman Filter for
  tightly coupled GNSS/UWB Integration},
    author={Qijia Zhao, Shaolin L"u, Jianan Lou, Rong Zhang},
    journal={arXiv preprint arXiv:2407.06915},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06915},
    primaryClass={cs.RO}
}