## 5 Experimental Design and Results Analysis
### System Accuracy Experimental Evaluation  
This research employed the freiburg2_desk_with_person sequence from the TUM dataset to assess the efficacy of the proposed approach. This series was acquired using a Kinect sensor and comprises RGB images, depth images, and ground truth trajectory data. This sequence presents formidable challenges including extensive scenes, intricate environments, dynamic objects, and motion loops. These characteristics make it an ideal testing platform for evaluating a SLAM system's pose estimation accuracy and loop closure detection effectiveness. Figure 9 illustrates the enhanced algorithm's posture estimation outcomes compared to ORB-SLAM3 under six degrees of freedom (6-DoF). The figure demonstrates that, in the translational direction, the enhanced algorithm's trajectory aligns considerably better with the ground truth trajectory than the ORB-SLAM3. Notably, the enhanced algorithm exhibits reduced error variations throughout the vigorous motion period from 420 s to 560 s, indicating robust stability. The improved method demonstrates a more accurate assessment of attitude angles in the rotational direction, particularly during significant pose alterations like abrupt turns, showcasing greater robustness. Our algorithm produces smoother and more continuous trajectories than the ORB-SLAM3, with better handling of sensor noise and motion uncertainty. Two principal metrics--Absolute Pose Error (APE) and Relative Pose Error (RPE)--were computed utilizing the EVO tool to assess the algorithm's effectiveness further. Figure 10 juxtaposes the APE time series of the two algorithms. The figure illustrates that the enhanced algorithm's error curve exhibits consistently reduced variations, with errors predominantly remaining below 0.0075 m and a maximum error peak of roughly 0.0175 m, considerably lower than the ORB-SLAM3's 0.025 m. The enhanced algorithm's mean and median error values were also around zero, signifying high consistency between the estimated and actual trajectories. Figure 11's RPE comparison results indicate that the enhanced algorithm exhibits a more concentrated distribution of relative errors between consecutive frames, with the error fluctuations typically remaining below 0.01 m. Additionally, the envelope area of the error curve is significantly smaller than that of the ORB-SLAM3, accompanied by a narrower standard deviation range, thereby illustrating the algorithm's superiority in local consistency and reliability. Figure 12 effectively illustrates the comprehensive trajectory comparison results. The primary view distinctly illustrates the trajectory variations between the two algorithms during the complete motion process. In contrast, three localized enlarged views concentrate on trajectory specifics at the corners and straight portions. In all critical domains, the trajectory of the enhanced algorithm (blue line) exhibits strong alignment with the ground truth trajectory, particularly at corners where the enhanced algorithm's trajectory shifts seamlessly and organically. In contrast, the ORB-SLAM3's trajectory (green line) exhibits significant offsets and distortions in these regions. Figure 9: Comparison between ground truth and estimated poses: (**a**) X-axis displacement; (**b**) Y-axis displacement; (**c**) Z-axis displacement; (**d**) roll angle; (**e**) pitch angle; (**f**) yaw angle. Figure 11: Comparison of relative trajectory errors for freiburg2_desk_with_person: (**a**) ORB-SLAM3 algorithm; (**b**) proposed algorithm. Figure 12: Comparison of the trajectory accuracies. The yellow boxes and yellow arrows in the figure are used to enlarge key areas and mark trajectory deviations, highlighting the accuracy differences among different algorithms in these regions; The red arrows in the small images on the right demonstrate that our algorithm achieves higher accuracy compared to other algorithms. Figure 10: Comparison of the absolute trajectory errors for freiburg2_desk_with_person: (**a**) ORB-SLAM3 algorithm; (**b**) proposed algorithm.