Simultaneous　Localization　And　Mapping　(SLAM)　algorithms　play　a　critical　role　in　autonomous　exploration　tasks　requiring　mobile　robots　to　autonomously　explore　and　gather　information　in　unknown　or　hazardous　environments　where　human　access　may　be　difficult　or　dangerous.　However,　due　to　the　resource-constrained　nature　of　mobile　robots,　they　are　hindered　from　performing　long-term　and　large-scale　tasks.　In　this　paper,　we　propose　an　efficient　multi-robot　dense　SLAM　system　that　utilizes　a　centralized　structure　to　alleviate　the　computational　and　memory　burdens　on　the　agents　(i.e.　mobile　robots).　To　enable　real-time　dense　mapping　of　the　agent,　we　design　a　lightweight　and　accurate　dense　mapping　method.　On　the　server,　to　find　correct　loop　closure　inliers,　we　design　a　novel　loop　closure　detection　method　based　on　both　visual　and　dense　geometric　information.　To　correct　the　drifted　poses　of　the　agents,　we　integrate　the　dense　geometric　information　along　with　the　trajectory　information　into　a　multi-robot　pose　graph　optimization　problem.　Experiments　based　on　pre-recorded　datasets　have　demonstrated　our　system＇s　efficiency　and　accuracy.　Real-world　online　deployment　of　our　system　on　the　mobile　vehicles　achieved　a　dense　mapping　update　rate　of　similar　to　14　frames　per　second　(fps),　a　onboard　mapping　RAM　usage　of　similar　to　3.4%,　and　a　bandwidth　usage　of　similar　to　302　KB/s　with　a　Jetson　Xavier　NX.