A Novel Data Association Approach for SLAM of Mobile Robot

Abstract

Data association is significantly important for SLAM. The correct data association may improve the accuracy of localization. In this paper we present a new approach for the Data Association problem of SLAM, based upon K-means Clustering and JCBB. First, The measurements at every step are separated into several groups. The number of groups is decided by the characteristics of the environment. JCBB and ICNN is then used on each group for getting several local matching results. Finally, we combine the local matching results by JCBB or ICNN together and find result that has the best joint compatibility as the best global matching result. The experiment results show that this approach can achieve that the matching accuracy is similar to JCBB and the matching time is far better than JCBB.