# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 1 Introduction
However, low running rate and inability to deal with objects that the neural network has not yet trained are two unavoidable disadvantages that need more research. Therefore, this paper focuses on the real-time V-SLAM method, which is not based on deep learning. For the method based on consistency detection, researchers search for consistency between static backgrounds or dynamic objects, such as geometric or physical consistency and so on. Compared with learning-based dynamic V-SLAM, this kind of SLAM usually has better real-time performance. Some researchers try to find the dynamic objects in a single scene. [PERSON] et al. [37] used scene flow to distinguish moving objects from the static scene, and the calculation of the scene flow was based on a result estimated by VO. [PERSON] and [PERSON] [38] presented an RGB-D SLAM based on static point weighting (SPWSLAM), which extracted the feature points at the depth edges of the RGB-D frames. In addition, some studies have tried to use multi-scene contrast to find dynamic objects. For example, [PERSON] et al. [39] proposed dense visual odometry and SLAM (DVO), which found the dynamic region by minimizing the photometric error between two consecutive RGB-D frames and used a robust error function to reduce the influence of large residuals. [PERSON] and [PERSON] [40] presented an effective background model-based DVO, which obtained the static region in images by computing the depth differences between consecutive RGB-D frames. [PERSON] et al. [41] proposed a novel motion removal SLAM (MotionRemove), which computed the camera ego-motion using RANSAC-based homography estimation with two consecutive RGB-D images. Then, the moving-object motions are roughly detected by subtracting the current RGB-D frame with the ego-motion-compensated last RGB-D frame. [PERSON] et al. [42] proposed a dynamic SLAM (DSLAM), which used Delaunay triangulation to connect mapped points to surrounding points and then treated the two points that remain connected in the next several frames as two points on the same rigid object, finally assuming the largest set of points that remain connected as a static background. There are some other improved V-SLAM methods based on consistency detection. For example, [PERSON] and [PERSON] [43] used two moving cameras to identify dynamic objects and considered the objects tracked successfully by one camera but unsuccessfully by the other camera in successive frames as moving objects but did not consider the occlusion problem under different viewing angles. [PERSON] et al. [44] used PWC-net to obtain optical flow from RGB-D images. The error of optical flow was also judged by the initial relative poses of the front and back frames, and the optical flow with a large error was regarded as a dynamic object. Based on the KinectFusion dense SLAM system, [PERSON] et al. [45] calculated the residual of each pixel and then obtained the approximate dynamic region by adaptive threshold segmentation, finally obtaining the final dynamic region and the static background map by morphological processing. However, it still needs a lot of research on parameter judgment and threshold setting. It is worth noting that many researchers are trying to use both deep learning and consistency detection in dynamic SLAM. As introduced above, the dynamic SLAM using consistency detection can usually run at high speed, but it is usually not guaranteed to completely screen out the small dynamic region in the image, and it is still disturbed in some scenes where objects are moving slowly. Thus, we propose an improved V-SLAM to reduce the impact of moving objects in a dynamic environment. In the proposed method, a concept of map point reliability is presented, which is used to describe the map points with high observation times as reliable map points. In addition, a concept of unreliable map points is presented, which is used to define the map points with fewer observation times. Based on the two concepts of the map points, we deal with those obvious and unobvious dynamic objects in V-SLAM.