# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 1 Introduction  
Visual SLAM (VSLAM) technology enables accurate perception of the environment and localization of robots. With the continuous innovation of technology and algorithms, SLAM has developed extensively. SLAM has become a fundamental technology in various application fields, such as unmanned vehicles, virtual reality, and unmanned aerial vehicles [1]. As the development of application areas and sensor technologies progresses, the demand for high-precision maps and accurate positioning is increasing. As a foundational technology in many fields, SLAM has garnered significant attention and research from scholars. Most traditional VSLAM research primarily focuses on static environments, as seen in open-source SLAM algorithms such as ORB-SLAM [2], SVO [3], LSD-SLAM [4], and PTAM [5], which have made significant progress in static environments. However, the accurate estimation of camera pose can be affected by the dynamic nature of moving objects in real-world scenarios, which may lead to erroneous localizations and maps. Consequently, several challenges persist in practical applications, such as the lack of real-time performance shown by enhanced algorithms, the limited map-building accuracy, and the prevalence of trajectory errors. Further development of VSLAM algorithms that are more robust, adaptable, and practical in dynamic environments holds important practical significance. In recent years, the combination of deep learning technology and VSLAM has shown good development prospects. Unlike traditional SLAM methods, VSLAM combined with deep learning can address many challenging SLAM issues [6]. Deep learning techniques,which have seen substantial progress in computer vision areas like object detection and semantic segmentation, can simultaneously predict multiple classes and the positions of bounding boxes. This capability facilitates high-speed object detection, making it highly suitable for real-time dynamic processing scenarios. Therefore, deep learning can be utilized to perform preliminary segmentation of object positions and semantic information in dynamic environments, identifying potential dynamic objects. Additionally, some deep learning methods, such as DeepFeat [7] and LIFT [8], utilize end-to-end training approaches, enabling better adaptation to varying viewpoints, changes in lighting, and image deformations. These networks improved the accuracy and robustness of SLAM systems by extracting feature points that contain richer semantic and geometric information. Therefore, the application of deep learning in VSLAM has important implications. This paper proposes a new VSLAM method for application in dynamic environments, which has been tested on the open-source dataset of the Technical University of Munich (TUM) [9]. Compared with related SLAM algorithms, this method improves operational speed and mapping accuracy. The main contributions of the work are as follows:  
1. On the basis of the ORB-SLAM2 algorithm, we propose replacing the traditional oriented features from accelerated segment test (FAST) and oriented FAST and rotated BRIEF (ORB) method with the GCNv2 [10] network to achieve feature point extraction and matching, which improves the system's efficiency and robustness. 2. The YOLOv5(You Only Look Once [11] v5) network combined with depth information is applied to VSLAM to obtain masks of potential dynamic objects, which helps identify regions containing dynamic objects. The standard deviation of depth values for all potential dynamic feature points is statistically calculated, and the key points are compared with the standard deviation of dynamic points to determine whether they are dynamic feature points. Based on this deviation, key points are either retained (if static) or discarded (if dynamic). 3. Experiments were conducted on the above content as follows. First, the accuracy and robustness of the feature exaction were evaluated by validating the feature point detection. Then, we analyzed the detection performance of the object detection network and the generated masks.