Visual　simultaneous　localization　and　mapping　(VSLAM)　is　one　of　the　foremost　principal　technologies　for　intelligent　robots　to　implement　environment　perception.　Many　research　works　have　focused　on　proposing　comprehensive　and　integrated　systems　based　on　the　static　environment　assumption.　However,　the　elements　whose　motion　status　changes　frequently,　namely　short-term　dynamic　elements,　can　significantly　affect　the　system　performance.　Therefore,　it　is　extremely　momentous　to　cope　with　short-term　dynamic　elements　to　make　the　VSLAM　system　more　adaptable　to　dynamic　scenes.　This　paper　proposes　a　coarse-to-fine　elimination　strategy　for　short-term　dynamic　elements　based　on　motion　status　check　(MSC)　and　feature　points　update　(FPU).　First,　an　object　detection　module　is　designed　to　obtain　semantic　information　and　screen　out　the　potential　short-term　dynamic　elements.　And　then　an　MSC　module　is　proposed　to　judge　the　true　status　of　these　elements　and　thus　ultimately　determine　whether　to　eliminate　them.　In　addition,　an　FPU　module　is　introduced　to　update　the　extracted　feature　points　according　to　calculating　the　dynamic　region　factor　to　improve　the　robustness　of　VSLAM　system.　Quantitative　and　qualitative　experiments　on　two　challenging　public　datasets　are　performed.　The　results　demonstrate　that　our　method　effectively　eliminates　the　influence　of　short-term　dynamic　elements　and　outperforms　other　state-of-the-art　methods.　(c)　2022　SPIE　and　IS&T