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 calcu-lation 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.
simultaneous localization and mapping(SLAM)visual SLAM(VSLAM)multi-robot SLAMmobile robotmulti-source data fusionsemantic
万方数据
维普
