Feature selection is always an important issue in the visual SLAM (simultaneous location and mapping)literature.Considering that the location estimation can be improved by tracking features with larger value of visible time,a new feature selection method based on motion estimation is proposed.First,a k-step iteration algorithm is presented for visible time estimation using an aftme motion model; then a delayed feature detection method is introduced for efficiently detecting features with the maximum visible time.As a means of validation for the proposed method,both simulation and real data experiments are carried out.Results show that the proposed method can improve both the estimation performance and the computational performance compared with the existing random feature selection method.