Abstract: It is difficult for traditional monocular simultaneous localization and mapping(MonoSLAM) methods to deal with the accumulated errors effectively,so how to fuse data from an inertial measurement unit(IMU) with data from a MonoSLAM system to reduce such errors becomes an important issue currently.We observed that,among the three Euler angles from an IMU,Roll and Pitch are more accurate than Yaw.Then if Yaw is used directly in a MonoSLAM system,the accumulated errors will increase rather than decrease.To address this problem,a new monocular SLAM method based only on Roll and Pitch output from an IMU is presented.First,the IMU-vision system is calibrated by using only Roll and Pitch.Then,the extended Kalman filter(EKF) is adopted to estimate the state vector comprising the camera position,Yaw of the IMU and the locations of features,and a SLAM system based on only Roll and Pitch is implemented robustly in real time.Experimental results show that the proposed method can reduce the accumulated errors effectively and alleviate the negative influence of the IMU's measurement errors on the system's stability.