# Visual SLAM Mapping Based on YOLOv5 in Dynamic Scenes
## 1 Introduction
This method uses the semantic module to segment the image and then uses the geometric module to detect and eliminate the dynamic objects. [PERSON] et al. [20] used a combined geometric and semantic approach for the detection of dynamic object features, which first uses a semantic segmentation network for the segmentation of dynamic objects, and then uses an approach based on the geometric constraint method to reject the segmented dynamic objects. [PERSON] et al. [21] accelerated and generated the semantic information necessary for the SLAM system by using a low-latency backbone while proposing a new geometrically constrained method to filter the detection of dynamic points in image frames, where dynamic features can be distinguished by random sampling consistency (RANSAC) using depth differences. [PERSON] et al. [22] proposed a motion detection and segmentation method to improve localization accuracy, while using a mask repair method to ensure the integrity of segmented objects, and using the Lucas-Kanade optical flow (LK optical flow) [23] method for dynamic point rejection in the rejection of dynamic objects. [PERSON] et al. [24] proposed a Pyramid Scene Parsing Network (PSPNet) detection thread based on the pyramid optical flow method combined with semantic information. In dynamic feature detection, PSPNet is used as the thread of semantic information detection to carry out semantic annotation on dynamic objects. An error-compensation matrix is designed to improve the real-time performance of the system, reducing the time consumed. [PERSON] et al. [25] proposed a dynamic dense construction method using 3D reconstruction errors to identify static and dynamic objects by studying the construction of dynamic environments. To improve the robustness and efficiency of dynamic SLAM mapping, [PERSON] et al. [26] proposed a method of combining geometric information with semantic information. Firstly, a residual model combined with a semantic module was used to perform semantic segmentation for each frame of an image. At the same time, a feature point classification method was designed to detect potential dynamic objects. [PERSON] et al. [27] proposed a map-building method that includes static maps and dynamic maps, in which dynamic maps consist of motion trajectories and points, and the correlation between maps is used to distinguish static points from dynamic points as a way to eliminate the influence of dynamic points in the map-building process. [PERSON] et al. [28] eliminated the effect of errors on map-building localization in dynamic environments by improving the mode of sensor-to-image acquisition and by selecting the specifications of the sensor for a particular environment. [PERSON] et al. [29] proposed a dynamic image segmentation network for the detection of dynamic objects in a dynamic environment. They proposed a dynamic image segmentation network for the detection of dynamic objects in a dynamic environment. et al. [29] proposed a PLD-SLAM combined with deep learning (for semantic information segmentation) and a K--Means clustering algorithm considering depth information (to detect the underlying dynamic features for the dynamic SLAM map building method) in order to solve the problem of incomplete or over-segmentation of images caused by deep learning while using point and line features to estimate the trajectory. [PERSON] et al. [30] combined the tracking algorithm and target detection, using the limit geometric constraint method to detect static points, while at the same time introducing depth constraints in the system to improve the robustness of the system. However, these methods have certain drawbacks when performing in dynamic environments, and there are various unavoidable disadvantages. When using geometric methods for dynamic point rejection, problems such as incomplete detection of dynamic points and non-detection of low dynamic points can easily occur, which in turn affects the accuracy of map building and positional accuracy.