Aiming to address the problem of decreased localization accuracy or even failure in visual simultaneous localization and mapping systems caused by object occlusion in highly dynamic environments, this paper proposes a dynamic VSLAM algorithm based on a joint geometric-motion error model and trajectory prediction. Unlike methods that rely on semantic segmentation or optical flow estimation, this approach fuses camera and IMU information to jointly model the epipolar geometric error and IMU pre-integrated motion error, and employs a probabilistic model for dynamic object detection and occlusion state estimation, maintaining high robustness under occluded conditions. To improve the continuity and accuracy of dynamic object tracking, an Extended Kalman Filter-based trajectory prediction is introduced for object pose estimation. Meanwhile, a joint factor graph model is constructed to optimize the camera, map points, and dynamic object feature points, where a dynamic motion-smoothing factor is designed to suppress abrupt object motion and reduce accumulated errors. Finally, experiments on the KITTI tracking dataset and real-world scenarios demonstrate that, compared with geometry-based and object-tracking-based dynamic SLAM methods, the proposed algorithm achieves superior pose estimation accuracy and dynamic object tracking performance in object occlusion scenarios within highly dynamic environments.