Ruan, Xiaogang; Zhang, Jingjing; Zhu, Xiaoqing; Zhou, Jing Simultaneous localization and mapping data association based on maximum expectation clustering for Gaussian mixture model. (Chinese. English summary) Zbl 1463.68120 Control Theory Appl. 37, No. 2, 265-274 (2020). Summary: Data association is the premise and basis of state estimation of mobile robot simultaneous localization and mapping (SLAM). In order to solve the problem of complex and time-consuming computation of joint compatibility branch and bound algorithm, an SLAM data association algorithm based on Gaussian mixture model maximum expectation clustering is proposed. Firstly, in order to reduce the number of observations participating in the association at the same time, we group the current measurements using maximum expectation clustering algorithm for Gaussian mixture model in the local region. Secondly, we conduct data association using joint compatibility branch and bound algorithm for each group. Finally, we obtain the optimal correlation result by combining the correlation result between each observation group and the local map features. The simulation results show that the SLAM data association algorithm based on Gaussian mixture model maximum expectation clustering guarantees the accuracy of data association, the computational complexity of this method is reduced and the running time is shortened. MSC: 68T40 Artificial intelligence for robotics 62H30 Classification and discrimination; cluster analysis (statistical aspects) Keywords:simultaneous localization and mapping; data association; joint compatibility branch and bound; Gaussian mixture model; expectation-maximization clustering; mobile robot PDFBibTeX XMLCite \textit{X. Ruan} et al., Control Theory Appl. 37, No. 2, 265--274 (2020; Zbl 1463.68120) Full Text: DOI