Simultaneous　Localization　and　Mapping　(SLAM)　is　the　core　technology　enabling　mobile　robots　to　autonomously　explore　and　perceive　the　environment.　However,　dynamic　objects　in　the　scene　significantly　impact　the　accuracy　and　robustness　of　visual　SLAM　systems,　limiting　its　applicability　in　real-world　scenarios.　Hence,　we　propose　a　real-time　RGB-D　visual　SLAM　algorithm　designed　for　indoor　dynamic　scenes.　Our　approach　includes　a　parallel　lightweight　object　detection　thread,　which　leverages　the　YOLOv7-tiny　network　to　detect　potential　moving　objects　and　generate　2D　semantic　information.　Subsequently,　a　novel　dynamic　feature　removal　strategy　is　introduced　in　the　tracking　thread.　This　strategy　integrates　semantic　information,　geometric　constraints,　and　feature　point　depth-based　RANSAC　to　effectively　mitigate　the　influence　of　dynamic　features.　To　evaluate　the　effectiveness　of　the　proposed　algorithms,　we　conducted　comparative　experiments　using　other　state-of-the-art　algorithms　on　the　TUM　RGB-D　dataset　and　Bonn　RGB-D　dataset,　as　well　as　in　real-world　dynamic　scenes.　The　results　demonstrate　that　the　algorithm　maintains　excellent　accuracy　and　robustness　in　dynamic　environments,　while　also　exhibiting　impressive　real-time　performance.