In low-cost strapdown inertial navigation systems (SINS), the determination of horizontal attitude relies on the specific force measurements of accelerometers to track the gravity vector. However, the specific force measured by accelerometers couples motion acceleration and gravitational acceleration under the maneuvering conditions. Both components are difficult to separate effectively, leading to the increased errors of gravityreferenced horizontal attitude estimation and severely restricting the practicability and reliability of low-cost SINS in dynamic scenarios. To address this issue, this paper proposes a low-cost horizontal attitude measurement method aided by the external-velocity, which avoids the complex dynamic modeling and specific scenario assumptions. The core of this method is to use the external velocity information to directly estimate and compensate the horizontal attitude errors caused by motion accelerations. First, the SINS update algorithm and error model are simplified according to the accuracy characteristics of microelectro-mechanical system (MEMS) inertial sensors. Second, an analytical relationship between the east/north velocities and the horizontal misalignment angle is established. Accordingly a measurement equation is constructed using the external velocity information to directly estimate the horizontal misalignment angle induced by the motion acceleration, thereby enabling the realtime correction of horizontal attitude. To verify the effectiveness of proposed method, both the turntable tests simulating a swaying environment and vehicle tests under the real maneuvering conditions were conducted. Experimental results indicate that the horizontal attitude measurement accuracy of the proposed method is better than 0.02° (RMS), which is comparable to that of the gravity-referenced method in the swaying environment. Under the maneuvering conditions, the root mean square errors (RMSE) of pitch and roll are reduced by 44.8% and 47.3% compared with the conventional integrated navigation algorithms. These results demonstrate the effectiveness and robustness of the proposed method in dynamic environments.