In the actual simultaneous localization and mapping (SLAM) application scenario, in order to solve the problem that a large number of imaging feature points of moving objects participate in feature tracking, which reduces the accuracy and robustness of the algorithm, as well as the problem that the traditional dynamic SLAM scheme with the strategy of eliminating dynamic features has insufficient residual static features and affects the SLAM effect, a dynamic vision-inertial integrated navigation method based on semantic segmentation and geometric constraints is proposed. A priori dynamic masks are created using the semantic segmentation network and the dynamic trust degree of various object types. Feature points are then extracted using an improved method of suppressing prior dynamic features. The real dynamic of feature points is then assessed using inertial measurement unit (IMU) pre-integration in conjunction with geometric constraint technology, and a feature point elimination strategy is developed for elimination. Finally, the remaining static feature points are used for tracking and positioning. Compared with the ORB-SLAM3, the positioning accuracy of the algorithm is improved by 73.05% on average in the indoor dynamic scene dataset TUM, and 19.85% in the outdoor dynamic scene dataset KITTI. Additionally, the accuracy is higher than that of the conventional dynamic SLAM approach.