## 5 Discussion  
### Performance Comparison Experiment  
To demonstrate the advantages of the proposed method, this section compares it with other existing approaches. Our method utilizes a two-stage feature matching strategy that combines LoFTR with SuperPoint+LightGlue models. We performed comparative experiments in real-world scenarios using various algorithms. Specifically, during the image matching phase, we employed LoFTR and SuperPoint+LightGlue as standalone feature matching algorithms and carried out pose estimation based on their matching results. The experimental outcomes are presented in Table 7.  
Table 7 presents the results of repositioning experiments conducted using only LoFTR and SuperPoint + LightGlue in Scene 1 and Scene 2. The experimental findings show that when relying solely on feature matching, the errors observed are significantly larger compared to those obtained with the proposed repositioning method. This highlights the limitations of using a single feature matching algorithm in complex environments. In contrast, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scene** & **RPE\_Mean (m)** & **RPE\_RMSE (m)** & **ATE\_Mean (m)** & **ATE\_RMSE (m)** \\ \hline Scene1 & 0.2985 & 0.4553 & 0.5071 & 0.6139 \\ Scene2 & 0.2459 & 0.3431 & 0.4089 & 0.4541 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Indoor relocalization experiment results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scene**} & **Input** & \multicolumn{4}{c}{**Translation Error (m)**} \\ \cline{3-5}  & **Image** & **LoFTR** & **SP+LG** & **OURS** \\ \hline \multirow{5}{*}{Scene1} & Test1 & 0.471 & 0.351 & **0.208** \\  & Test2 & 5.419 & 0.664 & **0.411** \\  & Test3 & 4.902 & 2.913 & **0.384** \\  & Test4 & 1.385 & 0.640 & **0.256** \\  & Test5 & 0.559 & 0.521 & **0.442** \\  & Test6 & 1.236 & 1.236 & **1.061** \\  & Test7 & 0.945 & 0.884 & **0.825** \\  & Test8 & 0.774 & 0.874 & **0.751** \\ \hline \multirow{5}{*}{Scene2} & Test1 & 0.625 & 3.665 & **0.583** \\  & Test2 & 0.219 & 0.220 & **0.205** \\ \cline{1-1}  & Test3 & 0.911 & 0.553 & **0.544** \\ \cline{1-1}  & Test4 & 0.496 & 0.442 & **0.428** \\ \cline{1-1}  & Test5 & 1.097 & 1.392 & **0.464** \\ \cline{1-1}  & Test6 & 0.477 & 0.301 & **0.265** \\ \cline{1-1}  & Test7 & 1.785 & 2.098 & **0.303** \\ \cline{1-1}  & Test8 & 1.851 & 1.415 & **0.228** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Translation Error Comparison for Different Feature Matching Methods. Bold values represent the best results among all methods. the proposed method combines the advantages of both LoFTR and SuperPoint + LightGlue, creating a two-stage feature matching process. LoFTR provides robust global matching capabilities, while SuperPoint + LightGlue excels in local feature extraction and matching. By leveraging the complementary strengths of both methods, our algorithm demonstrates higher precision and stability across various environments. In the indoor scene experiments, the proposed relocalization algorithm was evaluated against two widely used feature matching approaches: LoFTR and the combination of SuperPoint with LightGlue. Figures 14 and 15 present the relocalization performance of various algorithms in Scene 1 and Scene 2, respectively. A comparison of the resulting trajectories clearly indicates that the standalone use of LoFTR or SuperPoint combined with LightGlue performs significantly worse than the feature matching algorithm proposed in this study. To quantitatively assess the performance differences between the proposed algorithm and traditional methods, the Relative Pose Error (RPE) and Absolute Trajectory Error (ATE) metrics were calculated and compared, as shown in Tables 8 and 9 The results demonstrate that the errors obtained using LoFTR or SuperPoint combined with LightGlue individually  
Specifically, the proposed method achieves a low error rate in diverse test environments, demonstrating strong generalization and robustness.