# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 2 Related Work  
At present, many vSLAM schemes already have excellent performance in static environments. Some examples are monocular camera schemes [9; 10], binocular camera schemes [11] and RGB-D camera schemes [12]. With the popularity of a series of depth cameras such as Kinect and Releasense, RGB-D cameras are widely used in vSLAM schemes. Compared to monocular and binocular cameras, depth cameras can perform direct ranging. In addition, depth cameras can compensate for the high computational complexity of pure vision schemes. ORB-SLAM2 enjoys widely acclaim in the vSLAM field as a complete open-source scheme. ORB-SLAM2 determines the correspondence between the 2D ORB points of the current frame and the 3D points of the local map by feature matching. Then, the 3D coordinates of the matched points in the local map are projected to the current frame to estimate the camera pose by minimize the reprojection error. Finally, a global sparse map is built by bundle adjustment (BA). In order to run vSLAM in a specific static environment, some researchers had designed variants of the vSLAM scheme. [PERSON] et al. [13] proposed an affine transformation-based ORB feature extraction method (Affine-ORB) which is used and applied to existing robot vision SLAM methods. [PERSON] et al. [14] designed an RGB-D SLAM system that works specifically in structured environments, aiming to improve tracking and mapping accuracy by relying on geometric features extracted from the surroundings. [PERSON] et al. [15] designed a mobile mapping system installed on a motorized tricycle to count on-street parking statistics using RGB-D camera and GPS system. [PERSON] et al. [16] built a SLAM system using RGB-D cameras that are suitable for working in complex orchard environments. [PERSON] et al. [17] combined the Grid-based Motion Statistics (GMS) with the RANSAC algorithm to jointly filter the feature matching set acquired by ORB to improve the accuracy of ORB-SLAM2 poses. However, all of the above-mentioned works work in static environments. When dynamic objects are present in the environment, the system performance of vSLAM will deteriorate several times or even tens of times [18]. Dynamic objects usually affect the inter-frame pose estimation and mapping of vSLAM systems. The matching relationship between feature points on dynamic objects is usually an important factor that causes pose estimation errors. [PERSON] et al. [19] combined a GMS feature point matching method with a k-means clustering algorithm to reject feature points on dynamic objects. [PERSON] et al. [20] cluster regions in each image frame using depth information and intensity information. Then, dynamic feature points are eliminated by checking the consistency of motion of feature points in each region. In recent years, many scholars consider the incorporation of deep learning into vSLAM as the key to solve the dynamic environment problem. The semantic information in the images obtained by deep learning further improves the perception and understanding of the environments. [PERSON] et al. [21] proposed DynaSLAM based on ORB-SLAM2, which used the Mask-RCNN [22] network to achieve dynamic detection and background restoration functions. [PERSON] et al. [23] proposed the DS-SLAM, which uses SegNet [24] to perform semantic segmentation to obtain semantic masks. Later, [PERSON] et al. [25] improved the segmentation accuracy of DS-SLAM through PSPNet network [26] to achieve higher pose estimation accuracy in dynamic environments. [PERSON] [27] et al. proposed Dynamic-SLAM, which uses convolutional neural networks to build Single Shot MultiBox Detector (SSD) [28] to detect dynamic objects. [PERSON] et al. [29] took the lead in applying weakly supervised semantic segmentation to the dynamic detection of vSLAM. The drawback of this approach is that motion detection takes too much time, which affects the real-time performance of SLAM. [PERSON] et al. proposed the VDO-SLAM [30], which does not rely on the a priori information of motion.