Sparse extended information filter for feather-based SLAM

This paper presents an approach to binocular vision simultaneous localization and mapping (SLAM) based on sparse extended information filter (SEIF) algorithm, which is deduced by the sparsification treatment to EIF algorithm. SIFT (Scale Invariant Feature Transform) method is used to extract the Natural 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. the system has been implemented and tested on a mobile robot. This method used in vision-SLAM shows that the computational complexity of the SEIF algorithm is a constant, which is independent of environment features. That means SEIF has a high value of application in large-scale environment with a large number of features.