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.