# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) [1] is considered to be the basis for intelligent mobile robots to perceive their surroundings and perform tasks such as navigation, obstacle avoidance, and path planning. Currently, SLAM has been used in many industries, such as auto pilot and virtual reality [2]. The main problem solved by SLAM is that of mobile robots using sensors to build maps while estimating their pose in unknown environments [3]. At present, the main research directions of SLAM can be divided into two categories, laser SLAM and vSLAM. Compared with the expensive sensors of laser SLAM, vSLAM is popular among researchers for its price advantage. The sensors commonly used for vSLAM are monocular cameras, binocular cameras, and depthcameras. Furthermore, vSLAM can use the image information captured by the camera for semantic perception of the surrounding environment [4].  
At present, many achievements have been made in traditional vSLAM. However, these vSLAM schemes are worked under the assumption of a static environment. Dynamic objects in real scenes can degrade the performance of vSLAM [5]. In addition to this, the dynamic objects in the maps are not conducive to robot navigation or man-machine interaction [6]. Therefore, the effect of dynamic objects on vSLAM cannot be ignored.  
With the development of deep learning, many scholars have integrated methods such as object detection and semantic segmentation into vSLAM. These methods can improve the scenario understanding of vSLAM and provide new directions for the development of vSLAM [7]. In terms of tracking, semantic information can assist inter-frame matching to obtain more robust results. In terms of mapping, vSLAM can build semantic maps by combining semantic information in images. The maps with semantic information can give mobile robots a higher understanding of the environment.  
For the effect of dynamic objects on vSLAM, a semantic information-based optimized vSLAM is proposed with ORB-SLAM2 [8] as the base framework. In the process of frame matching, in order to improve the accuracy of pose estimation in dynamic sceneries, the detection of dynamic regions that are based on homography matrix and dense optical flow methods are used to remove feature points in dynamic regions. In the process of mapping, the semantic information in the image is obtained using the deep learning method. Then, a region growing algorithm combined with depth information is used to improve the accuracy of object semantic segmentation. Finally, the 3D information of dynamic objects is removed using semantic information and dynamic region detection to avoid building dynamic objects into the map. The main contributions of this paper are as follows:  
1. A strategy that enables vSLAM initialization in dynamic environments is proposed, which is able to initialize local maps and poses robustly and efficiently.  
2. An efficient method for inter-frame pose estimation in dynamic environments based on homography matrix and dense optical flow method is proposed.  
3. A method of optimized semantic segmentation based on depth images is proposed for accurately annotating semantic objects in key frames.  
The remainder of this paper is organized as follows: Section 2 discusses the related research on vSLAM in dynamic environments. Section 3 presents the proposed method for localization and building maps for vSLAM in indoor dynamic scenes. Section 4 validates the proposed method using publicly available datasets and real scenario. Finally, Section 5 summarizes this study.