## 2 Related Work  
The evolution of filter-based SLAM techniques has steadily advanced, driven by the pursuit of greater localization and mapping accuracy. Early approaches, such as EKF-SLAM, established a foundational framework but struggled with scalability due to their high computational complexity. More recent innovations have built upon this groundwork, enhancing robustness in challenging settings. For instance, [PERSON] et al.'s multi-sensor fusion particle filtering [8] and [PERSON] et al.'s anti-degeneracy scheme for LiDAR SLAM [9] have improved performance in indoor and feature-scar esnsvironments. Despite these strides, challenges like computational intensity remain, particularly in dynamic contexts, as highlighted by [PERSON] and [PERSON] [10]. Hybrid methods, such as [PERSON] et al.'s integration of EKF with visual data [11], offer incremental gains but still fall short in highly dynamic scenarios, where adaptability remains a limiting factor.  
In parallel, graph optimization has emerged as a powerful tool, significantly boosting SLAM precision and scalability. Pioneering techniques like Sparse Bundle Adjustment and ORB-SLAM introduced 7-DoF pose graph optimization and loop closure, effectively reducing drift in static scenes. Building on these ideas, recent developments have pushed the boundaries further. [PERSON] et al.'s GNSS graph optimization for LiDAR SLAM [12] and [PERSON] et al.'s S-Graphs 2.0 with hierarchical-semantic optimization [13] deliver improved accuracy and computational efficiency. Meanwhile, [PERSON] et al.'s DALI-SLAM employs multi-constraint graph optimization to tackle degeneracy in LiDAR-inertial SLAM [14], and [PERSON] et al.'s LDG-CSLAM leverages factor graphs to enable multi-robot collaboration [15]. Yet, these methods often rely on static scene assumptions, which can undermine their effectiveness in dynamic real-world environments.  
The advent of deep learning has brought transformative potential to visual SLAM, particularly in handling dynamic settings. For example, [PERSON] et al. integrate YOLOv8-based segmentation with sparse optical flow to enhance environmental perception, though this comes at the cost of increased computational demands [16]. Similarly, [PERSON]'s approach uses multi-scale prediction and optical flow to track object motion in drone-based SLAM, but its complexity limits real-time applicability [17]. Object detection strategies have also advanced, with [PERSON] et al.'s YOLOv8 paired with geometric constraints [18] and [PERSON] et al.'s DFT-VSLAM using dynamic optical flow tracking [19] improving dynamic object detection--though multi-object scenarios remain a challenge. Cutting-edge innovations like [PERSON] et al.'s [2] Dyna-OpenVINS with multi-scale semantic segmentation and [PERSON] et al.'s LTD-SLAM employing multi-scale YOLOv8 [20] further refine feature extraction and localization accuracy, often sacrificing real-time performance in the process.  
Drawing inspiration from these advancements, our work bridges SLAM-specific techniques with broader progress in computer vision. The challenge of managing dynamic features in SLAM echoes the multi-scale feature extraction tasks in object detection, as seen in Feature Pyramid Networks (FPNs) [21] and HRNet [22], which excel at detecting objects across diverse scales. Likewise, motion tracking methods, such as the Lucas-Kanade (LK) optical flow approach, have proven adept at analyzing dynamic scenes. Our proposed MSF-SLAM adapts these concepts, incorporating multi-scale feature extraction through the MSFConv module and dynamic feature filtering via the DOFF framework. This tailored solution strikes a balance between accuracy and efficiency, meeting the real-time demands of visual SLAM in dynamic environments with a practical and effective approach.