To address the problem that traditional Kalman filter(KF)cannot accurately approximate nonlinear functions via first-order Taylor expansion in regions with strong nonlinearity

which leads to large deviations in estimated positions

a global navigation satellite system(GNSS)/inertial measurement unit(IMU)navigation algorithm based on manifold preintegration and factor graph is proposed.First

the algorithm optimizes IMU preintegration technology using Lie group algorithms and processes inertial measurement values at keyframes; then

it constructs IMU preintegration factors

GNSS factors

and state prior factors to achieve multi-source information fusion;finally

it limits the sliding window optimization range

performs nonlinear optimization on variable nodes within the window

and outputs the final navigation results.Simulation experimental results show that the proposed method exhibits better convergence performance in nonlinear regions.Compared with KF

the positioning accuracy of the proposed method is improved by 91.55%

91.06%

and 90.84% in the northward

eastward

and straight-line distance

respectively;compared with the extended KF(EKF) algorithm

the positioning accuracy is improved by 59.98%

75.50%

and 73.37%

respectively.