In a complex observation environment, the GNSS signal of the GNSS/INS integrated navigation system is susceptible to be disturbed and result in a rapid decline in the accuracy of INS independent navigation. Aiming at the above problems, this paper studies the odometer-assisted GNSS/INS integrated navigation algorithm based on the factor graph, uses the odometer observation information combined with the non-holonomic constraint to construct the heading speed constraint equation, and adopts factor graph optimization for parameter estimation which conducts multiple linearization calculations and multiple iterations at the same time. The results of real vehicle experiments show that when the GNSS signal is good, the factor graph-based method has a faster convergence time than the filtering method, and the convergence speed is increased by about 10 times; when the GNSS signal is interrupted, the positioning accuracy of odometer-assisted integrated navigation system in the E and U direction has increased by 83％ and 89％ respectively. And compared with the conventional Kalman filter method, the positioning accuracy in the E and N can respectively be improved by using factor graph optimization in this paper. There are 63％ and 70％ improvements.
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