# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 1 Introduction  
SLAM is a technology for self-positioning and environment map construction in unknown environments with sensors on machines [1, 2, 3]. In the early research of SLAM, researchers described the problem of SLAM as a state estimation problem of spatial uncertainty, and used an extended Kalman filter (EKF), a particle filter (PF) and maximum likelihood estimation (MLE) to eliminate the uncertainty in the process of positioning and mapping [4, 5, 6]. In 2009, [PERSON] and [PERSON] [7] realized the sparsity and symmetric structure of the Hessian matrix in bundle adjustment (BA) and published their research results, which enabled BA to be accelerated. Since then, BA as the core graph optimization algorithm began to replace the filtering algorithm to become the mainstream method to eliminate uncertainty. Visual SLAM (V-SLAM) uses the camera as the main sensor and only obtains input information from the image [8, 9]. Compared with other LiDAR or inertial sensors, it has the advantages of rich information, low cost, lightweight and small volume. At present, V-SLAM is widely used in autonomous driving, AR (augmented reality)/VR (virtual reality), and other fields [10, 11, 12]. The framework of V-SLAM is mainly divided into two parts: visual front-end and back-end optimization. The visual front-end is also commonlyreferred to visual odometry (VO) [13; 14]. The main function of VO is to calculate the rough poses of the camera and generate new map points. The back-end optimization uses general graph optimization (g\({}^{2}\)o) [15] to optimize the data passed from the front-end to eliminate random errors. There are two kinds of VO: the feature-based method and the direct method. The feature-based method extracts representative points from the frames and then uses them to estimate the camera poses and build the map. The feature-based method has high robustness because it can still identify the same points when the scene and camera perspective slightly change. However, for the feature point with higher robustness, its calculation consumes higher resources. The representative SLAM of the feature-based method is the ORB-SLAM series algorithms [16; 17]. The direct method SLAM estimates the pose and builds the map according to the brightness information of the pixels, which consumes fewer resources. The direct SLAM can generate a dense or semi-dense map. However, it is sensitive to the image quality [18; 19; 20]. Direct sparse odometry (DSO) [21] is the representative SLAM of the direct method SLAM. At present, the feature-based method is the mainstream method of VO in V-SLAM. At present, most of the high robustness and high efficiency of V-SLAM are achieved in a static environment. However, the real world is complex and dynamic most of the time. The movement of people, animals and vehicles may cause V-SLAM to fail in feature matching and loop closure detection. To deal with these problems, researchers have come up with various processing methods to find moving objects in images [22; 23]. One method is based on deep learning, and the other is based on consistency detection. Deep-learning-based methods use deep neural networks to identify objects that are thought to move with high probability, such as Mask R-CNN [24], SegNet [25] and you only look once (YOLO) [26; 27] and then track and maintain these objects or cover them with a mask [28; 29]. To reduce the impact of the neural network on the real-time performance of V-SLAM, some researchers run the neural network in an independent thread [23; 30] or just use the neural network in some specifically selected frames and spread the detection results to other frames by feature points matching [31; 32; 33]. More recent methods based on deep learning tend to go further by not just removing the dynamic foreground, but also inpainting or reconstructing the static background that is occluded by moving targets [34; 35; 36]. Benefiting from the rapid advances in deep learning and easy embedding into V-SLAM, this kind of dynamic SLAM works well in most scenarios.