In　Simultaneous　Localization　and　Mapping　(SLAM)　algorithms,　it　is　common　to　assume　static　environments.　However,　Static　assumption　may　cause　the　SLAM　system　to　drift　or　even　crash　in　many　practical　scenarios　where　there　are　many　moving　objects.　Combining　SLAM　with　dynamic　object　estimation　can　significantly　improve　system　stability　in　dynamic　environments.　In　this　paper,　we　propose　a　visual　SLAM　system　for　dynamic　scenes　that　achieves　precise,　robust　camera　pose　estimation　and　dynamic　object　tracking.　By　utilizing　deep　learning-based　object　detection　and　scene　flow　feature　point　tracking　technologies,　dynamic　objects　are　separated　from　static　ones.　Then,　dynamic　and　static　objects　arejointly　optimized,　resulting　in　3D　coordinates　of　feature　points　as　well　as　the　6　degree-of-freedom　state　representation　of　dynamic　objects　in　space.　Through　experiments,　our　proposed　method　has　been　shown　to　achieve　higher　accuracy　and　robustness　in　highly　dynamic　scenes.　©　2023　IEEE.