A Tightly Coupled UWB/PDR Fusion Positioning Algorithm for Indoor Environments

Currently accurate indoor location services have become an important need for people. Many new indoor positioning methods have been developed. Among them, the more prominent ones are pedestrian dead reckoning (PDR) algorithm and localization algorithm based on ultrawideband (UWB) signal. However, the PDR algorithm has a cumulative error, and the UWB is greatly affected by the environment. Fusion localization is performed based on the complementarity of the two localization methods. Commonly used fusion means are fusion methods based on Kalman filtering, but the method only considers the current moment and the previous moment of the state is susceptible to anomalous measurements. Therefore, this article proposes a tightly coupled UWB/PDR fusion localization algorithm based on factor graph optimization (FGO) with sliding window. The method takes more account of the influence of historical measurements and state quantities to make the localization results more accurate and reliable. Moreover, the sliding window is added to improve the operation efficiency of the algorithm. In order to verify the effectiveness of the proposed algorithm, field tests are conducted in three scenarios, and the experimental results show that the algorithm has improved the positioning accuracy compared with the single localization means and the fusion algorithm based on tightly coupled Kalman filter. Also, the operation efficiency is improved compared to that without sliding window.