An improved association method of SLAM based on ant colony algorithm

A new data association algorithm based on ACA (ant colony algorithm) is proposed to solve the data to deal with the data association problem for SLAM (simultaneous localization and mapping). Using the advantages of ACA in resolving the problem of combination and optimization, the problem of data association was transformed into combinational optimization problem and the ACA together with JML (joint maximum likelihood) theory was used to associate the measurements and features. The detailed approach was given and the algorithm model was constructed. At last, the presented algorithm was tested under certain simulation environment. The results show the superiority of the presented method in data association of SLAM. It reduces computation cost and maintains better association efficiency and it is an available method to deal with the problem on data association of SLAM.