Data association method of SLAM based on ant colony algorithm
一种基于蚁群算法的SLAM数据关联方法

A new data association algorithm based on Ant Colony Algorithm (ACA) was proposed to deal with the data association problem for Simultaneous Localization And Mapping (SLAM). 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 ant colony algorithm was used to associate the measurements and features together with Joint Maximum Likelihood (JML) theory. 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 a feasible method to deal with the problem on data association of SLAM.