Research on Binocular Vision SLAM with Odometer in Indoor Environment

Abstract

With the aim of solving the low positioning accuracy and low robustness problems of vision SLAM algorithm,Extended Kalman Filter(EKF) method based on binocular vision and odometer is proposed in this paper.Feature point can be obtained by extracting image features with improved SIFT algorithm,and the vision feature map is constituted.SLAM is completed by using the information of binocular vision and robot position with EKF.This method can either solve the monocular vision inaccuracy problem of feature point information obtained by special initialization method or avoid the enormous computation brought about by binocular vision odometer using image information to restore movement as well as the in-robust disadvantages of motion estimation.The results from simulation experiments indicate that in the unknown indoor environments,this algorithm operation is stable,and the positioning accuracy is high.