The number of ( ultra-wide band) UWB ranging measurements received by pedestrians to be located in the complex indoor scene is uncertain. To address this issue, the INS / UWB tight integrated localization algorithm based on a factor graph is proposed. It can be used to fuse UWB ranging measurement from random accessing node and exiting node. Firstly, an INS / UWB tightly integrated factor graph model is constructed, which is based on the pedestrian motion model and the UWB measurement model. Due to the simultaneous modeling of pedestrian position and velocity, there are cycles in the factor graph model. Aiming at the factor graph model with cycles, the sum-product algorithm (SPA) is used to derive the message passing algorithm among different nodes in the factor graph model through two iterations, and the posterior probability density of pedestrian position and velocity is calculated. Furthermore, given the rapid enlarging error deduced by a special ranging measurement vector in the INS / UWB tight integrated localization algorithm, an improved factor graph algorithm based on coordinate transformation is proposed. Simulation results show that the proposed INS / UWB tightly integrated localization algorithm can effectively fuse dynamic UWB ranging measurements in complex indoor scenes. On the premise of meeting the demand of computation and memory consumption, the proposed algorithm can improve positioning accuracy and speed estimation accuracy by 14. 94% and 56. 42% , respectively, compared with the extended Kalman filter ( EKF) algorithm with multi-models.