# Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes
## 1 Introduction  
With the rapid development of robotics and computer application technology, Simultaneous Localization and Mapping System (SLAM) [1, 2] technology has been widely used in agriculture, unmanned driving, aerospace, and other fields. As one of the most advanced technologies in the field of mobile robotics, SLAM utilizes a variety of sensors on the robot to conduct localization, navigation, and map construction in the environment. A robot must build a map of its environment to realize autonomous positioning and navigation in an unfamiliar environment. Therefore, for the robot, the construction of an environmental map is the foundation of all follow-up work, and an accurate map can enable the robot to conduct accurate autonomous positioning. With in-depth research, SLAM technology has also been greatly developed, and many SLAM mapping technologies have emerged, such as VSLAM [3], ORB-SLAM [4], ORB-SLAM2 [5], DynaSLAM [6], ORB-SLAM3 [7], RGBD SLAM [8, 9, 10], DS-SLAM [11], and so on. However, most of the mapping construction in the SLAM system is based on a static environment. When drawing in a static environment, the ORB-SLAM2 system is one of the classic drawing frames of the SLAM system. The ORB-SLAM2 system has the advantages of a simple structure and fast running speed. Therefore, this research is based on ORB-SLAM2. Most of the existing SLAM systems operate in a static environment, and most of them suffer from low accuracy of map building, large trajectory errors, and poor robustness of the system when building maps in a dynamic environment. As most of our environments are dynamic (i.e., they are static only in specific circumstances), map construction and positional trajectories in dynamic environments are even more important. In the process of researching dynamic SLAM map building [12, 13], many scholars have conducted in-depth research on SLAM systems in a dynamic environment, put forward many improvement methods, put forward new methods in the process of SLAM research,and achieved certain results. However, in practical applications, problems such as poor real-time performance of the improved algorithms, low accuracy of map building, and large trajectory errors still exist and have not been fundamentally solved. [PERSON] et al. [14] constructed a Single Shot MultiBox Detector (SSD) object detector based on a convolutional neural network and prior knowledge when eliminating dynamic objects in a dynamic environment, and used this SSD object detector to detect moving objects in a dynamic environment. At the same time, a missing detection compensation algorithm was added to optimize object detection. Finally, map construction and pose estimation were carried out in the SLAM system. [PERSON] et al. [15; 16] used a semantic optical flow SLAM method to detect and reject dynamic objects in the environment, and at the same time used a segmentation Network (SegNet) to mask the semantic information, and then filter the object features, detect the dynamic object, and segment the dynamic object. The dynamic points were then eliminated by judging the feature points that have mobility without a priori dynamic markers. The obtained static features were then used to build the map. [PERSON] et al. [17] proposed a method to delete RGB-D data when removing feature points in a dynamic environment, and applied this improved method before employing SLAM. In the process of drawing construction, the method proposed by the author was used to eliminate the dynamic data, to achieve the effect of removing the dynamic features. [PERSON] et al. [18] used an enhanced semantic segmentation network to detect and segment dynamic features of dynamic SLAM environments as a way to achieve dynamic feature rejection. [PERSON] et al. [19] adopted a static and dynamic image segmentation method to improve the accuracy of image construction to reduce the interference of dynamic objects in image construction.