基于概率选取随机特征点的单目视觉SLAM方法

Visual Monocular SLAM Based on Probabilistic Selection of Random Feature Points

针对机器人单目视觉同时定位与地图构建(SLAM)问题,利用SIFT特征点,结合反向深度估计法,提出基于概率统计SIFT特征点的随机选取方法.在保证特征点分布相对均匀的前提下,有效控制了特征点的总体数量,减少了单目视觉EKF-SLAM(扩展卡尔曼滤波SLAM)方法的应用限制.实验研究表明,该特征点选取方法对不同场景均具有较高的稳定性,并且一定程度上提高了算法的收敛速度.

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 pro- posed 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.