Correspondence　pruning　has　recently　drawn　considerable　attention　as　a　crucial　step　in　image　matching.　Existing　methods　typically　achieve　this　by　constructing　neighborhoods　for　each　feature　point　and　imposing　neighborhood　consistency.　However,　the　nearest-neighbor　matching　strategy　often　results　in　numerous　many-to-one　correspondences,　thereby　reducing　the　reliability　of　neighborhood　information.　Furthermore,　the　smoothness　constraint　fails　in　cases　of　large-scale　rotations,　leading　to　misjudgments.　To　address　the　above　issues,　this　paper　proposes　a　novel　robust　correspondence　pruning　method　termed　RoSe,　which　is　based　on　rotation-invariant　sequence-aware　consensus.　We　formulate　the　correspondence　pruning　problem　as　a　mathematical　optimization　problem　and　derive　a　closed-form　solution.　Specifically,　we　devise　a　rectified　local　neighborhood　construction　strategy　that　effectively　enlarges　the　distribution　between　inliers　and　outliers.　Meanwhile,　to　accommodate　large-scale　rotation,　we　propose　a　relative　sequence-aware　consistency　as　an　alternative　to　existing　smoothness　constraints,　which　can　better　characterize　the　topological　structure　of　inliers.　Experimental　results　on　image　matching　and　registration　tasks　demonstrate　the　effectiveness　of　our　method.　Robustness　analysis　involving　diverse　feature　descriptors　and　varying　rotation　degrees　further　showcases　the　efficacy　of　our　method.　©　2024　ACM.