To address the large-scale environmental mapping,lightweight robot swarms are employed to perceive the environment and multi-robot collaborative SLAM (Simultaneous Localization and Mapping) scheme has been developed to solve the problems of high individual cost,global error accumulation,excessive concentration of calculation and risk perplexed single robot SLAM schemes,which has strong robustness and stability.Here,we review the history of multi-robot collaborative SLAM,and introduce its fusion method and architecture.The current collaborative SLAM approaches are sorted out from the viewpoint of machine learning classification.The future development trends of multi-robot SLAM in directions of deep learning,semantic maps,and multi-robot VSLAM are projected.