## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) is a foundational technology for autonomous robotic systems, enabling precise navigation and mapping in unknown environments [1]. Visual SLAM, leveraging affordable and information-rich camera sensors, has emerged as a critical approach due to its ability to provide detailed spatial understanding, supporting applications such as robotic exploration, augmented reality, and autonomous navigation [2]. With advancements in computational power and sensor technology, systems like ORB-SLAM3 have achieved remarkable performance in static environments by relying on robust feature extraction and graph-based optimization [3]. However, these conventional visual SLAM frameworks encounter significant challenges in dynamic indoor settings, where moving objects--such as people or shifting furniture--introduce noise that disrupts feature tracking and degrades pose estimation accuracy [4].  
The limitations of traditional visual SLAM in dynamic environments arise from several factors, including the assumption of scene rigidity, the unpredictability of object motion, and the difficulty in distinguishing static from dynamic features [5]. These issues lead to increased trajectory errors and reduced mapping reliability, hindering the deployment of SLAM systems in real-world scenarios where dynamic elements are prevalent. Over the years, researchers have proposed various strategies to address these challenges, ranging from geometric modeling and semantic understanding to the integration of deep learning techniques [6,7]. Despite these efforts, achieving robust and accurate localization in highly dynamic scenes remains an open problem, necessitating innovative solutions that balance performance, efficiency, and adaptability.  
In this paper, we introduce Multi-Scale Feature SLAM (MSF-SLAM), a novel framework designed to enhance visual SLAM performance in dynamic environments. MSF-SLAM integrates a Multi-Scale Feature Consolidation (MSFConv) module into the YOLOv8 object detection network to improve feature extraction across multiple scales, coupled with a Dynamic Object Filtering Framework (DOFF) within ORB-SLAM3 that employs the Lucas-Kanade (LK) optical flow method to eliminate dynamic feature points. This dual approach ensures robust detection of moving objects and precise retention of static features critical for pose estimation. The key contributions of this work include:  
1. Enhanced Feature Extraction: The MSFConv module augments YOLOv8, enabling superior detection of dynamic objects with minimal computational overhead.
2. Dynamic Feature Management: The DOFF leverages LK optical flow to filter out dynamic features, enhancing ORB-SLAM3's robustness in real-time dynamic scenes.
3. Extensive Validation: Comprehensive experiments on the TUM RGB-D [link] dataset demonstrate significant improvements, with pose estimation accuracy increasing by up to 94.43% over ORB-SLAM3 in dynamic sequences.  
The remainder of this paper is organized as follows: Section 2 reviews related work on visual SLAM and dynamic environment handling. Section 3 describes the MSF-SLAM framework, including the MSFConv and DOFF components. Section 4 presents experimental results and analysis. Finally, Section 5 concludes with key findings and future directions.