## 2 Related Work  
Before deep learning was widely used in SLAM and VO, several researchers applied geometric constraints or statistic methods to deal with the problem of SLAM or VO systems working in dynamic environments. In 2007, [PERSON] et al. [17] proposed a method that integrating the least-squares formulation and sliding window optimization with generalized expectation maximization. Both dynamic and static objects are directly incorporated into the estimation to maintain a consistent estimate. In 2012, [PERSON] et al. [18] presented Dynamic Pose Graph SLAM (DPG-SLAM), which uses incremental smoothing and mapping (iSAM) as the underlying SLAM state estimation engine to maintain an up-to-date map even though the environment is changing. In the same year, [PERSON] et al. [19] proposed a novel CoSLAM. By equipping multiple cameras on different platforms which move independently but work together, the system completes the intercamera pose estimation and intercamera mapping in dynamic scenes. It works robustly, but the video data must be processed offline. [PERSON] et al. [20] presented a probabilistic formulation in 2013, which employs sensors and motion models with custom probability distributions to complete the camera motion estimation from RGB-D images robustly and directly. In 2015, a dense 3D SLAM using Kinect in dynamic scene was presented by [PERSON] et al. [21]. Based on a constant motion model, pixels whose absolute velocity exceeds the threshold probably belong to moving objects and are taken as seed segmentation. Then a region growing procedure is performed to identify dynamic regions by adapting these seeds. [PERSON] et al. [22] proposed a RGB-D SLAM method based on depth edge for dynamic environments in 2017. Only depth edge points are used to estimate the camera motion. The static weight, which represents the possibility of a point belonging to the static region, is combined with the intensity assisted iterative closest point (IAICP) algorithm to handle highly dynamic environment. In 2018, [PERSON] et al. [23] presented a novel method to segment and track dynamic objects in dynamic environments. By using ML-RACSAC, the states which include velocity and the position of multiple moving objects can be robustly estimated in an unknown environment. With the rapid development of deep learning technology in various fields, especially in the field of computer vision such as object detection and semantic segmentation, quite a few researchers have tried to make use of the image semantic information extracted by deep learning technology to improve the performance of SLAM or VO in dynamic environments in recent years, especially in the past two years. In 2017, [PERSON] el al. [12] combined CNN objection detection with feature-based monocular SLAM. The system adopts YOLOv2 [24] to detect the dynamic objects in the scene. Then the feature points belonging to dynamic objects are eliminated and static features are remained for further data association and graph optimization. In 2018, [PERSON] et al. [13] proposed a front end to segment dynamic content by using multi-view geometry and Mask R-CNN [25]. They combined this front end with ORB-SLAM2 [8] to improve the robustness of SLAM in monocular, stereo and RGB-D configurations. In the same year, DS-SLAM was presented by [PERSON] et al. [14]. The system integrates SegNet [26] and moving consistency check to detect dynamic objects and build a dense semantic 3D octo-tree map. [PERSON] et al. [15] also proposed a semantic SLAM for dynamic environments based on object detection and an improved octo-tree map in 2018. In 2019, there are also some researchers carrying out some superior work. [PERSON] et al. [3] proposed Semantic Optical Flow SLAM (SOF-SLAM), which is built on RGB-D mode of ORB-SLAM2 [8]. The pixel-wise semantic segmentation obtained from SegNet [26] is used as a mask in the semantic optical flow to calculate a credible fundamental matrix, then the dynamic features are removed on the basis of the matrix. [PERSON] et al.