Abstract. Simultaneous localization and map building (SLAM) is a key problem for an intelligent robot to accomplish autonomous navigation. And the results of data association are of key importance to the success of the SLAM. In this study, two bottleneck issues constraining SLAM data association are discussed in detail. One issue is the filtering data association of SLAM, the other issue is the loop closure of SLAM. The research status of the two issues is explained in detail. On this basis, the inadequacies of existing research are pointed out. And some advices for future research are provided. The work of this paper is of a certain reference value for both comparison of existing data association studies in SLAM and future intensive SLAM research.

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