A Novel Data Association Approach of SLAM

Abstract

A novel data association algorithm based on max-min ant system (MMAS) is proposed to solve the data associations of SLAM. By the advantages of MMAS in resolving the general assignment problem (GAP), the problem of data association was transformed into the problem of combination and optimization, and the ant colony algorithm was used to associate the measurements with features according to the joint compatible rule. At last, the presented algorithm was compared with other data association methods. The results obtained show the superiority of the presented method in data association of SLAM. It reduces computation cost efficiently on the condition of remaining certain correct associations, and it is an available method to deal with the problem on data association of SLAM.