Simultaneous　Localization　And　Mapping　(SLAM)　is　a　critical　technology　for　autonomous　driving　in　urban　environments.　However,　in　environments　with　many　moving　objects,　currently　available　LiDAR-based　SLAM　methods　cannot　effectively　detect　loops　as　they　assume　a　static　environment,　resulting　in　unreliable　trajectories.　Therefore,　in　this　paper,　we　propose　RB-LIO,　a　LiDAR　SLAM　system　based　on　the　LIO-SAM　framework.　The　proposed　system　utilizes　a　dynamic　object　segmentation　module　to　mitigate　the　influence　of　moving　objects　with　tight-coupled　LiDAR　inertial　odometry.　It　also　corrects　the　complete　6-DoF　loop　closure　with　BoW3D　and　performs　pose　graph　optimization　using　GTSAM-ISAM2.　We　tested　RB-LIO　on　public　datasets　(KITTI　and　MulRan)　and　self-collected　datasets　and　compared　it　with　state-of-the-art　SLAM　systems　such　as　A-LOAM,　LeGO-LOAM,　LINS,　LIO-SAM,　and　Fast-LIO2.　The　experimental　results　indicate　that　RB-LIO　achieves　more　than　40%　improvement　in　accuracy　and　a　significant　improvement　of　map　quality.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.