This study proposes an attitude solution method based on the fusion of an adaptive extended Kalman filter and a Mahony filter to address the limitations of low-cost inertial measurement units，such as restricted accuracy，high noise，and severe drift. The proposed method employs the Mahony filter to estimate the attitude in real time while using the adaptive extended Kalman filter to adjust the process and measurement noises dynamically，thereby optimizing the attitude estimation results. The effectiveness of the proposed algorithm was validated through static experiments，attitude accuracy tests，and experiments in real-world scenarios. The experimental results indicate that the fusion algorithm outperforms the fusion algorithm based on the extended Kalman and Mahony filters in terms of the pitch，roll，and yaw accuracy，achieving a 52.8% reduction in the closed-loop error in practical applications. This method effectively suppresses noise and drift，improves the attitude estimation accuracy，and provides a reliable solution for high-precision attitude determination in complex environments.