SUMMARYA 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.

中文翻译：

---

一种基于中心差分联合兼容准则和聚类的SLAM数据关联算法

摘要提出了一种基于中心差分联合相容性（CDJC）准则和聚类的同时定位与建图（SLAM）数据关联算法，以获得数据关联结果。首先，设计CDJC准则计算联合马氏距离。其次，识别聚类结构的排序点用于将所有观察到的特征分成几组。第三，CDJC 分支定界法被设计为在每个组中执行。基于仿真数据和基准数据集的结果表明，该算法计算复杂度低，为移动机器人的SLAM提供了准确的关联结果。