Abstract: Unmanned vehicle navigation in complex environments (such as urban canyons or forest trails)faces challenges such as GNSS signal blockage,multipath effects,and outlier interference.Traditional EKF methods exhibit limitations in addressing these issues.Recently,factor graph optimization (FGO)has emerged as a research focus in the field of multi-sensor fusion,demonstrating superior global optimization capabilities and high accuracy.However,due to its reliance on the least-squares method,FGO lacks robustness against outliers,limiting its navigation performance in complex environments.This paper proposes a robust factor-graph-based optimization algorithm that combines Huber kernel function and chi square test for unmanned vehicle application scenarios in complex environments.The algorithm introduces wheel odometry (ODO)to assist GNSS+INS integrated navigation.Within the factor graph framework,ODO nodes are introduced as motion constraints,fusing observational data from GNSS and INS nodes.A robust kernel function is applied to enhance the algorithm's resistance to outliers.Experimental results show that the proposed algorithm achieves high accuracy and robustness in scenarios with strong multipath effects and GNSS signal outages,significantly improving navigation performance in complex environments.This provides a novel solution for high-precision unmanned vehicle navigation.

Key words: complex environments, robustness, factor graph optimization, wheel odometry, GNSS+INS, integrated navigation