To address the problems of inaccurate camera pose estimation and insufficient utilization of environmental semantic information in most SLAM systems in dynamic environments, proposes a dynamic object detection algorithm based on the instance segmentation, keyframe detection, and Bayesian dynamic feature probability propagation, and three-dimensional reconstruction of static objects in the environment. To construct a multi object monocular SLAM system in a dynamic environment, the system performs instance segmentation and feature extraction on key frame input images, which could obtain a set of potential moving object feature points and a set of static object feature points. A set of non-moving object feature points is used to obtain inter frame pose transformation, Bayesian probability propagation of dynamic and static feature points are utilized for ordinary frames, and a set of static feature points is used to achieve accurate estimation of camera pose. Joint data association is performed on static objects in key frames, and after sufficient data is available, multi object 3D reconstruction is performed to construct a multi object semantic map. Finally, multi object monocular SLAM is achieved. The experimental results on TUM and Boon public dataset show that in dynamic scenarios, compared to the ORB-SLAM2 algorithm, the RMSE of APE decreases by 54. 1% and 58. 2% on average.