Traditional　visual　simultaneous　localization　and　mapping　(SLAM)　algorithms　rely　on　the　scene　rigidity　assumption.　However,　when　dynamic　objects　exist　in　the　scene,　the　stability　of　the　SLAM　system　will　be　affected　and　the　accuracy　of　pose　estimation　will　be　reduced.　Currently,　most　of　the　existing　methods　apply　probability　strategies　and　geometric　constraints　to　reduce　the　impact　caused　by　a　small　number　of　dynamic　objects.　But　when　the　number　of　dynamic　objects　in　the　scene　is　high,　these　methods　will　fail.　In　order　to　deal　with　this　problem,　a　novel　algorithm　is　proposed　in　this　paper.　It　combines　the　dynamic　visual　SLAM　algorithm　with　the　multi-target　tracking　algorithm.　Firstly,　a　semantic　instance　segmentation　network　together　with　geometric　constraints　is　introduced　to　assist　the　visual　SLAM　module　to　effectively　separate　the　static　feature　points　from　the　dynamic　ones,　and　at　the　same　time,　it　can　also　achieve　the　better　multi-target　tracking　performance.　Furthermore,　the　trajectory　and　velocity　information　of　the　moving　objects　can　also　be　estimated,　which　can　provide　decision　information　for　autonomous　robots　navigation.　The　experimental　results　on　KITTI　dataset　show　that　the　localization　accuracy　of　the　proposed　algorithm　is　improved　by　about　28%　compared　with　ORB-SLAM2　algorithm　in　dynamic　environments.　©　2022　by　Journal　of　Data　Acquisition　and　Processing.