A Fast Joint Compatibility Data Association Algorithm for SLAM Based on Clustering

In order to quickly and accurately obtain data association results in simultaneous localization and mapping (SLAM) of mobile robot, a fast joint compatibility data association algorithm (DFJCBB) for SLAM based on DBSCAN (density-based spatial clustering of application with noise) is proposed. Firstly, the local association strategy is used to limit features in local map. Then, a density-based clustering method, that is DBSCAN method, is used to group all measurements at the current moment and get a number of measurement groups with small correlation, because the measurements appear a clear distribution in most environments. Finally, joint compatibility branch and bound (JCBB) algorithm is adopted in data association of each group to obtain the optimal association solution between each group of measurements and local map features, and the optimal association solutions are combined to obtain the final association result. The performance of the proposed algorithm is verified by simulation based on the simulator and benchmark dataset. The results show that the proposed algorithm can guarantee high association accuracy, reduce the computational complexity and shorten the running time. It is suitable for solving the data association problem of SLAM in different complex environments.