When the SLAM system estimates the camera position, a large number of feature points of moving objects participate in the feature tracking thread leading to a decrease in the accuracy and robustness of the algorithm, so how to efficiently and accurately reject the dynamic objects in the scene is particularly important. Existing dynamic vision SLAM algorithms may miss detecting or incorrectly recognize static objects as dynamic objects and reject them when dealing with dynamic objects, which triggers the problem of insufficient number of static feature points, thus affecting the stability and accuracy of the SLAM system. Therefore, this paper proposes a visual SLAM method based on panoptic segmentation and multi-view geometry, which uses panoptic segmentation FPN network to accurately recognize all objects in the segmented image, rejects a priori dynamic feature points and retains as many static features as possible, based on which LK optical flow method with fused image pyramid is used to realize optical flow