In order to solve the problem that the filtering accuracy is poor or even divergent when the inertial navigation system is affected by model errors and measurement outlier errors, a multi-sensor fusion algorithm based on square-root cubature Kalman filter(SRCKF)wdetection is proposed. The square-root cubature Kalman filter innovation sequence is adaptively adjusted by the covariance matching method, and the adjusted innovation will compensate the measurement noise variance matrix and reduce the influence of model error, then use the adjusted innovation to detect the error and improve the w- detection accuracy. And construct the observation value replacement criterion to replace the error observation value, so as to solve the influence of the measurement abnormal value error. Finally, the attitude fusion is carried out by using SRCKF, the attitude of the gyroscope is taken as the state equation, and the attitude of the accelerometer and magnetometer replaced by detection is taken as the measurement equation. The experimental results show that the proposed algorithm can accurately estimate the system attitude, and the average solution accuracy can be improved by 62. 43% compared with the traditional algorithm. Under different conditions, the overall performance of the algorithm can be greatly improved, and the attitude can be solved quickly to ensure the solution accuracy.