Abstract: [Purposes] In complex environments like urban canyons,GNSS signals are susceptible to multipath effects and blockages.Traditional extended Kalman filter algorithms often suffer from IMU bias estimation errors and filter divergence due to a lack of robust outlier handling and state constraints.This paper proposes an RTK/INS integrated positioning method based on adaptive regularization constraints. [Methods] A multi-level quality control mechanism is constructed： first,a motion consistency check model is employed to detect GNSS outliers online； second,the IGG-Ⅲ robust estimation theory is introduced to calculate equivalent weight factors.These factors are mapped to adaptive regularization parameters to dynamically adjust the constraint intensity on IMU bias states,thereby actively mitigating the impact of abnormal observations. [Findings] Vehicle experiments demonstrate that,compared with the traditional EKF loose coupling,the proposed method significantly enhances system stability.The positioning accuracy in the east and north directions is improved by 57.4% and 44.6%,respectively,with the overall root mean square error reduced by 45.0%. [Conclusions] The proposed method effectively ensures positioning accuracy and robustness in harsh observation environments．

Key words: GNSS/INS integrated navigation, RTK/INS loose coupling, motion consistency check, adaptive regularization, IGG-Ⅲ model