In　this　paper,　a　drift　rejection　SLAM　(Simultaneous　Localization　and　Mapping)　method　is　proposed　targeting　indoor　scenarios,　where　SLAM　generates　large　drifts　due　to　the　lack　of　reliable　features.　To　provide　sufficient　features,　we　leverage　multiple　feature　primitives　and　geometric　restraints　(parallel　or　perpendicular)　restraint　in　man-made　environments.　Under　some　satisfy　Manhattan　World　(MW)　assumption　scene,　such　as　corridors,　we　can　get　absolute　and　drift-free　rotation　estimation　using　a　Gaussian　sphere.　By　fully　utilizing　drift-free　rotation　estimation　under　MW　assumption　and　the　local　stability　of　purely　track　restricted　by　point,　line,　and　plane　features,　our　drift　rejection　SLAM　method　becomes　more　accurate　and　robust.　Additionally,　by　exploiting　the　constraint　of　planar　motion　on　ground　robot,　we　propose　an　ingenious　strategy　to　reduce　translation　drift　by　eliminating　vertical　movement　in　the　Manhattan　world.　Advantages　of　our　method　over　other　state-of-the-art　algorithms　are　validated　on　public　datasets　and　real-world　experiments.　The　code　is　released　at　https://github.com/WangWen-Believer/DR-SLAM.