In　recent　studies　on　LiDAR　SLAM,　the　achievement　of　robust　optimized　LiDAR　odometry　is　the　primary　objective.　For　the　mapping　part,　some　studies　focus　on　improving　the　processing　of　point　cloud,　while　others　aim　to　the　optimization　of　the　result　deviation　caused　by　errors.　Meanwhile,　in　the　fields　of　robotics　and　autonomous　driving,　multi-sensor　fusion　solutions　based　on　IMUs　are　becoming　the　norm.　This　paper　contributes　to　the　optimization　of　mapping　by　leveraging　a　lightweight　LiDAR-inertial　state　estimator.　The　proposed　method　combines　information　from　a　6-axis　IMU　and　a　3D　LiDAR　to　form　a　tightly-coupled　scheme　that　incorporates　iterative　error　state　Kalman　filter　(IESKF).　Due　to　the　continuous　error　accumulation,　trajectory　deviations　can　be　significant.　To　mitigate　this,　an　adaptive　distance　threshold　loop　closure　detection　mechanism　is　employed.　Furthermore,　since　the　algorithm　primarily　addresses　outdoor　scenes,　ground　features　collected　by　sensors　account　for　a　significant　portion　of　the　computation.　Improvements　in　the　ground　segmentation　method　lead　to　less　disturbance　during　mapping　on　uneven　terrain,　enabling　the　method　to　effectively　ad-dress　a　wider　range　of　real-world　environments.　As　a　result,　the　proposed　method　demonstrates　excellent　stability　and　accuracy,　as　verified　in　experiments　conducted　on　urban　dataset　and　campus　environment.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.