## 1 Introduction  
SLAM (Simultaneous Localization and Mapping) is a robotic methodology employed when a robot operates in an unknown environment [1]. Utilizing onboard sensors such as cameras, inertial sensors, and laser sensors, the robot captures information about the external surroundings [2, 3]. These acquired data are then used in real-time to estimate the robot's own pose and simultaneously construct a map of the surrounding environment. The camera possesses characteristics such as compactness, low-power consumption, and cost-effectiveness. Simultaneously, it has the capability to capture a more comprehensive set of environmental information. Consequently, the adoption of cameras as sensors for visual SLAM has emerged as a prominent and actively pursued research direction [4]. Research scholars have proposed several visual SLAM system frameworks [5], including the VINS-Mono [6] and ORB-SLAM series, which have shown promising results thus far [7]. Among them, the ORB-SLAM series has drawn a lot of interest from academics studying this area because of its benefits, which include strong stability and real-time performance. [PERSON] et al., released the open-source ORB-SLAM2 in 2017 that offers three camera modes for greater versatility [8]. In 2021, they even unveiled ORB-SLAM3 with a multi-map system that incorporates a feature-based inertial visual odometry that greatly enhances the system's localization accuracy [9]. However, there are still certain issues with ORB-SLAM3 in practical situations [10]. While moving objects in actual real-world environments can cause errors in the correlation of visual odometry data, existing algorithms typically assume the external environment to be static [11]. This affects the accuracy of the SLAM algorithm [12]. As a result, studying SLAM algorithms in dynamic environments is very crucial [13, 14]. To improve the robustness and accuracy of the SLAM system in dynamic environments, many researchers optimize the algorithm based on ORB-SLAM [15]. DynaSLAM [16], as proposed by [PERSON] et al., is a hybrid semantic segmentation and multi-view geometry method that segments dynamic objects by instances using semantic segmentation networks. This allows for precise feature point culling on dynamic objects and background repair. On the basis of ORB-SLAM2, DS-SLAM is integrated with a semantic segmentation network that greatly enhances localization accuracy in dynamic scenes by minimizing the impact of dynamic targets on the system via the motion consistency detection method [17]. DM-SLAM leverages both the distributed and local RANSAC (Random Sample Consensus) to extract static features from dynamic scenes while incorporating them into the system's initialization [18], based on the characteristics of both statics and object dynamics. In dynamic scenes, the system performs better than conventional SLAM systems. [PERSON] et al. proposed a visual SLAM using a deep feature matcher to extract features in each image frame by a deep feature extractor in a visual odometer and proposed a camera position fusion estimation method [19], which is able to operate at 0.08 s per frame with low error. [PERSON] et al. optimized the visual SLAM algorithm through semantic information, based on the framework of ORB-SLAM2, and added the dynamic region detection module into the visual odometer to improve the accuracy of the system's position estimation in dynamic environments. Then, the image is semantically segmented by the BiSeNetV2 network, dynamic objects are removed using semantic information and dynamic regions, and finally a map containing semantic information is constructed [20]. The existing SLAM system is susceptible to the influence of dynamic objects in dynamic scenes, which leads to a decrease in the system's localization accuracy. In this research, we propose a SLAM system for dynamic situations that determines whether a feature point influences the system accuracy and whether it should be rejected or retained [21].