Efficient Data Association for Vision-Based SLAM

Abstract

A new approach to vision-based simultaneous localization and mapping (SLAM) is proposed. the scale invariant feature transform (SIFT) features is landmarks, The minimal connected dominating set(CDS) approach is used in data association which solve the problem that the scale of data association increase with the map grows in process of SLAM . SLAM is completed by fusing the information of binocular vision and robot pose with Extended Kalman Filter (EKF).the system has been implemented and tested on data gathered with a mobile robot in a typical office environment. Experiments presented demonstrate that proposed method improves the data association and in this way leads to more accurate maps.