To address the issue of decreased attitude estimation accuracy caused by environmental interference and sensor noise affecting the attitude and heading reference system (AHRS), a noise data processing method based on variable structure error state Kalman filtering (VS-ESKF) is proposed. The text describes a method for detecting noise data in accelerometers and gyroscopes by analyzing the statistical characteristics of sensor observation data and innovation sequence in AHRS. The method is based on the acceleration norm and forgotten sequential probability ratio test (F-SPRT). Secondly, the smooth variable structure filtering (SVSF) strategy is introduced into the error state Kalman filtering (ESKF) to improve its processing capability on the uncertainty of the noise model, based on the noise detection results. The magnetic disturbances are evaluated and magnetometer compensation weights are adjusted in real-time using the Mahalanobis distance method to obtain accurate AHRS correction data by combining the magnetic field strength and magnetic inclination parameter characteristics. The designed VS-ESKF algorithm can detect AHRS noise data timely and accurately, and effectively suppress noise interference, as demonstrated by experimental validation based on a self-unicycle robot platform. Compared to the ESKF algorithm, the accuracy of estimating the roll angle, pitch angle, and yaw angle has increased by 31.05%, 32.32%, and 40.07%, respectively. This improvement enhances the accuracy and stability of attitude estimation.