The pedestrian dead reckoning (PDR) algorithm based on the mobile phone inertial sensors is one of the core methods for pedestrian navigation. However, due to factors such as the sensor noise, the positioning error of dead reckoning accumulates over time, which may lead to the divergence of the pedestrian positioning. To compensate the positioning error and improve the positioning accuracy of the PDR method, the GNSS positioning is generally introduced and combined with the conventional PDR method via the Kalman filter. In this article, a pedestrian collaborative positioning method based on the factor graph optimization is proposed. The state transition, measurements and collaborative ranging information are all used as state constraints. In addition, the optimal estimation is performed uniformly. To evaluate the performance of the method, experiments are implemented in both open-sky area and GNSS denied environment. The experimental analysis results show that the pedestrian collaborative positioning method based on the factor graph optimization can effectively improve the positioning accuracy both in open-sky and GNSS degraded area. Compared with the cooperative method based on Kalman filter, the maximum horizontal positioning error is reduced by more than 30% .