## 3 Methodology  
### Improved YOLOv8 Object Detection Algorithm  
ORB-SLAM3 is composed of three main threads: the front-end tracking thread, the back-end local mapping thread, and the loop closure detection thread. The front-end tracking thread is responsible for extracting and matching features from image frames to estimate the camera pose, and it optimizes the pose by matching the current frame against keyframes in the map. After processing the current frame, the system evaluates whether it should be added as a new keyframe. The back-end local mapping thread employs graph optimization techniques to improve map accuracy by optimizing the spatial relationships between the current frame and associated keyframes. Simultaneously, the loop closure detection thread detects overlaps between the current scene and previously visited areas by matching the current frame with historical keyframes, thereby performing loop closure optimization. This process reduces cumulative errors and enhances the global map's accuracy and consistency. Collectively, these threads enhance the long-term stability, robustness, and precision of the SLAM system. In this study, the proposed algorithm extends the ORB-SLAM3 framework by incorporating a dynamic object detection thread based on YOLOv8 and integrating a dynamic feature point removal module into the front-end tracking thread. The architecture of the enhanced ORB-SLAM3 framework is illustrated in Figure 1.  
This study employs the YOLOv8 model, recognized for its high accuracy and stability, as the core network for dynamic object detection. The architecture of YOLOv8 comprises three main components: the Backbone, the Neck, and the Head. The Backbone is responsible for feature extraction, employing a multilayer convolutional network to extract deep features from the input images. The Neck facilitates feature fusion by combining feature maps across multiple scales. Finally, the Head is dedicated to object detection and classification, generating the final recognition outputs. Despite its strengths, YOLOv8's existing convolutional structure has a relatively limited capacity for feature extraction when handling dynamic or moving objects. This limitation can lead to reduced detection accuracy for dynamic targets, diminishing the algorithm's effectiveness in scenarios involving mobile objects. To address this challenge and enhance YOLOv8's performance in dynamic object detection, this study introduces a Multi-Scale Feature Extraction Convolution module (MSFConv). The MSFConv module replaces the original convolutional structure, improving target detection accuracy without significantly increasing the model's parameter size. The architecture of the enhanced YOLOv8 network is shown in Figure 2.  
To improve YOLOv8's detection accuracy while minimizing the number of parameters, this study replaces the standard convolution in the original network with a custom-designed Multi-Scale Feature Convolution module (MSFConv). Unlike the single-scale convolution used in the original network for feature extraction, the MSFConv module employs a multi-scale design that incorporates convolutional kernels of varying sizes. This design enables the module to effectively capture both fine-grained details and global contextual features, enhancing detection performance across diverse scenarios. Furthermore, the MSFConv module incorporates depthwise separable convolution (DWConv), which preserves the spatial feature extraction capability while significantly reducing computational complexity and parameter overhead. The inclusion of a residual structure within the module enhances gradient propagation stability and strengthens the network's representational capacity. Consequently, the model can focus more effectively on critical features, delivering superior detection performance. The detailed architecture of the MSFConv module is presented in Figure 3.  
Figure 1: Improved ORB-SLAM3 Thread Structure. In the MSFConv module, feature maps are processed through a sequence of transformations and convolutional operations.