面向城市复杂环境的GNSS/INS高精度图优化算法

In the complex urban environment, GNSS receivers is easily affected by various factors such as building occlusion and multipath effects, lead to gross errors or rejection of the signal, which easily affects the accuracy and robustness of the GNSS/INS integrated navigation system. A factor graph optimization algorithm of GNSS/INS with gross error online detection is proposed to improve the performance of the integrated navigation system under the interference condition of urban environment. Based on the characteristics of correlation between information, a sliding window gross error detection and fitting replacement algorithm for satellite signals is proposed to suppress the influence of satellite gross errors. The GNSS position, velocity factor and improved IMU pre-integration factor are constructed to realize nonlinear optimization of integrated navigation information. The simulation and vehicle experiments show that the positioning error of the proposed algorithm is reduced by more than 90% compared with the extended Kalman filter and the traditional factor graph optimization algorithm in the case of gross errors in satellite signals, which can assist the navigation system to obtain a better state estimation effect. In the case of GNSS rejection, the positioning error of the algorithm is reduced by more than 30% compared with the extended Kalman filter algorithm.