An improved monocular simultaneous localization and mapping method is proposed to solve a series of problems existing in the conventional monocular SLAM system, which is based on the classical EKF filter and FAST corners. To reduce the state estimation error resulting from deviation of expansion point when linearization, the cameracentered iterated EKF is applied to monocular SLAM system, which can minimize linearized error by iterative updating and representing all feature locations in the current camera frame. For robustness and efficiency of tracking features, and a homogeneous distribution of feature points, ORB features, which have the property of fast detection and matching, and invariance to scale and rotation, are selected as the feature points. Moreover, cell division method through detection to selection is applied. And the utilization of inverse depth parameterization for point features can efficiently avoid the problem of scalable ambiguity when map initialization. In addition, 1point RANSAC approach can ensure the stability and precision of filter by eliminating the wrong feature matching. The performance of system is evaluated by the ground truth. Experiments show that this new method is more robust and precise in comparison with the other monocular SLAM solutions, and meets the realtime processing requirements. The absolute trajectory positioning precision increases to 2.24 m and the mean error over the trajectory increases to 1.3%. To sum up, the proposed method is a practical solution to monocular localization and mapping.