Simultaneous　localization　and　mapping　(SLAM)　is　one　of　the　core　technologies　for　intelligent　mobile　robots.　However,　when　robots　perform　VSLAM　in　dynamic　scenes,　dynamic　objects　can　reduce　the　accuracy　of　mapping　and　localization.　If　deep　learning-based　semantic　information　is　introduced　into　the　SLAM　system　to　eliminate　the　influence　of　dynamic　objects,　it　will　require　high　computing　costs.　To　address　this　issue,　this　paper　proposes　a　method　called　YF-SLAM,　which　is　based　on　a　lightweight　object　detection　network　called　YOLO-Fastest　and　tightly　coupled　with　depth　geometry　to　remove　dynamic　feature　points.　This　method　can　quickly　identify　the　dynamic　target　area　in　a　dynamic　scene　and　then　use　depth　geometry　constraints　to　filter　out　dynamic　feature　points,　thereby　optimizing　the　VSLAM　positioning　performance　while　ensuring　real-time　and　efficient　operation　of　the　system.　This　paper　evaluates　the　proposed　method　on　the　publicly　available　TUM　dataset　and　a　self-made　indoor　dataset.　Compared　with　ORB-SLAM2,　the　root-mean-square　error　of　the　Absolute　Trajectory　Error　(ATE)　can　be　reduced　by　98.27%.　The　system　successfully　locates　and　constructs　an　accurate　environmental　map　in　a　real　indoor　dynamic　environment　using　a　mobile　robot.　It　is　a　VSLAM　system　that　can　run　in　real-time　on　low-power　embedded　platforms.　©　2023　by　the　authors.