Monocular vision SLAM based on key feature points selection

Simultaneous localization and mapping (SLAM) is a key research content of robot autonomous navigation, the visual monocular SLAM based on Extend Kalman Filter(EKF) is one important method to handle this problem. But due to high computational complexity, it has strict limits on the number and stability of the feature points, traditional method selects few corners like or straight lines as feature points, and these methods limit the application scope of EKF-SLAM. This paper proposes a key points selection method based on SIFT(Scale-invariant feature transform) feature point, on the assumption of relative uniform of the feature points´ distribution, through controlling the total number of feature points effectively, the applied restriction of the visual monocular EKF-SLAM is reduced. Experiments show that this feature point selection method has a high stability for different scenes, and improves the convergence velocity.