Visual Monocular SLAM Based on Probabilistic Selection of Random Feature Points

For the problem of visual monocular robot SLAM(simultaneous localization and mapping),a random feature point selection method based on probability and statistics of SIFT(scale-invariant feature transform) feature points is proposed by using SIFT feature points and the inverse depth method.On the assumption of relative uniform distribution of the feature points,the total amount of feature points is constrained effectively,and the application restriction of the visual monocular EKF-SLAM(extended Kalman filtering SLAM) is relaxed.Experiments show that this feature point selection method is of high stability in different scenes,and improves the convergence speed to some extent.