## 4 Experiment and Results Analysis
### Pose Estimation Error Analysis Experiment  
(1) TUM Dataset  
To evaluate the performance of the proposed algorithm, we utilized the TUM RGB-D dataset [24], a widely recognized benchmark for visual SLAM systems, accessible at [[https://vision.in.tum.de/data/datasets/rgbd-dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset)]([https://vision.in.tum.de/data/datasets/rgbd-dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset)) (accessed on 21 June 2024). This dataset provides synchronized RGB and depth image pairs captured using a Microsoft Kinect sensor at a resolution of 640 \(\times\) 480 pixels and a frame rate of 30 Hz, alongside ground truth camera poses for accuracy assessment. The dataset includes a variety of indoor sequences designed to test SLAM algorithms under different conditions, such as static scenes, slow motions, and highly dynamic environments. In our experiments, we selected sequences from the \"fr3\" category, which are captured in a typical office environment and divided into two primary scene types: \"sitting\" and \"walking\". The \"sitting\" sequences feature slow actions, such as gesturing and conversing at a desk with minimal object motion, while the \"walking\" sequences involve significant indoor movements, including people walking, which introduce dynamic elements challenging for SLAM systems. Each scene type includes three distinct camera motion patterns: \"halfsphere\", where the camera moves in a hemispherical trajectory approximately 1 m in diameter; \"static\", where the camera remains stationary; and \"XYZ\", where the camera translates along the x, y, and z axes while maintaining a consistent orientation. These motion patterns test the algorithm's robustness across varying degrees of camera dynamics. Specifically, we evaluated the following sequences:  
1. sitting_static: Stationary camera with minor gestures. 2. sitting_xyz: Translational camera motion in a low-dynamic setting. 3. walking_half: Hemispherical camera path with multiple moving people. 4. walking_static: Stationary camera observing walking individuals. 5. walking_xyz: Translational camera motion with significant dynamic activity. These frame counts reflect the full sequences as provided in the TUM RGB-D dataset, though our evaluation used all available frames per sequence to ensure comprehensive testing across motion types. The \"sitting\" sequences (totaling \(\sim\)1700 frames across static and XYZ) represent low-dynamic conditions, while the \"walking\" sequences (totaling \(\sim\)3100 frames across halfsphere, static, and XYZ) provide high-dynamic challenges, allowing us to assess MSF-SLAM's performance in diverse scenarios. The ground truth information in the TUM RGB-D dataset was collected using a high-precision motion capture system consisting of eight infrared cameras operating at 100 Hz. This system tracks reflective markers attached to the Kinect sensor, providing accurate 6-DoF (degrees of freedom) pose estimates with sub-millimeter precision. The recorded RGB-D frames were temporally aligned with the motion capture data using timestamps, ensuring that each frame has a corresponding ground truth pose. This setup enables reliable evaluation of SLAM algorithms by comparing estimated trajectories against these externally measured poses, particularly valuable in dynamic scenes where internal sensor drift could otherwise skew results. To ensure a fair comparison, all algorithms (including ORB-SLAM3 and other baselines) were tested on the same sequences--e.g., \"walking_half\" and \"walking_xyz\"--using identical parameter configurations, such as feature extraction thresholds and optimization settings. This consistency allows us to isolate the impact of MSF-SLAM's enhancements on pose estimation accuracy across the specified camera motion types. Experimental Results Analysis  
To assess the experimental results, we employed two primary metrics: absolute trajectory error (ATE) and relative pose error (RPE), which evaluate the precision of pose estimation relative to the ground truth.