In order to solve the real-time problem of visual navigation system with traditional motion estimation algorithm, a new approach based on classified feature points for mobile robot motion estimation was proposed. For dividing feature points into far points and near points, the distances between feature points and mobile robot were calculated according to the 3-dimensional coordinates of feature points. The far points were sensitive to the rotational movement of robot, thus they were used to calculate rotational matrix; the near points were sensitive to translational motion, thus they were used to calculate the translational matrix. When the far points and the near points are 30% of original feature points, the proposed approach had equivalent accuracy but reduced 60% computing time compared with RANdom SAmple Consensus (RANSAC). The results demonstrate that, by using classified feature points, the proposed algorithm can effectively reduce computing time, meanwhile ensure accuracy of motion estimation, and it can meet the the real-time requirement with large feature points.