A Data Association Algorithm for SLAM Based on Central Difference Joint Compatibility Criterion and Clustering

Abstract

SUMMARY A data association algorithm for simultaneous localization and mapping (SLAM) based on central difference joint compatibility (CDJC) criterion and clustering is proposed to obtain the data association results. Firstly, CDJC criterion is designed to calculate joint Mahalanobis distance. Secondly, ordering points to identify the clustering structure is used to divide all observed features into several groups. Thirdly, CDJC branch and bound method is designed to be performed in each group. The results based on simulation data and benchmark dataset show that the proposed algorithm has low computational complexity and provide accurate association results for SLAM of mobile robot.