In　order　to　solve　the　problems　of　slow　detection　speed,　incomplete　perception　of　environmental　information　and　poor　system　robustness　in　a　single-robot　system,　a　method　for　autonomous　simultaneous　localization　and　mapping　in　an　unknown　environment　based　on　the　multi-robot　cooperation　is　proposed.　The　Rapidly-exploring　Random　Tree　(RRT)　is　applied　to　the　rapid　selection　of　frontier　points　in　multi-robot　systems.　With　the　improved　task　allocation　algorithm　based　on　market　mechanisms,　the　target　points　are　allocated　to　each　robot,　which　effectively　coordinates　the　behavior　among　the　robots　and　improves　the　efficiency.　Using　the　optimized　DBSCAN　(Density-Based　Spatial　Clustering　of　Applications　with　Noise)　algorithm　and　multi-sensor　data　to　obtain　the　initial　position　and　orientation　of　each　robot,　a　multi-robot　global　coordinate　system　is　established,　which　optimizes　the　map　fusion　process.　The　experimental　results　show　that　this　method　can　successfully　complete　the　multi-robot　autonomous　exploration　and　mapping　in　different　environment,　and　the　efficiency　is　higher　than　that　in　single-robot　systems.　Also,　the　method　is　implemented　and　tested　using　the　Robot　Operating　System　(ROS)　framework,　so　it　can　be　applied　to　practical　scenarios　and　extended　to　the　scenario　of　more　robots.　©　2018　IEEE.