## 4 Experiment
### Experiments in Indoor Real-World Scenarios  
Indoor visual relocalization presents considerable challenges due to environmental factors such as reflective floors, textureless regions, and strong lighting interference. These \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Actual Coordinates**} & \multicolumn{2}{c}{**Relocalization**} & \multicolumn{2}{c}{**Metrics**} \\ \hline
**Input** & **Measured Coordinates** & **Matching** & **Predicted Coordinates** & **Coordinate** & **Translation** \\
**Images** & **(N, E)** & **Image** & **(N, E)** & **Error** & **Error** \\ \hline Test1 & (2,526,947.321, 529,196.851) & Map03 & (2,526,947.469, 529,196.997) & (\(-\)0.148, \(-\)0.146) & 0.208 \\ Test2 & (2,526,950.403, 529,197.401) & Map09 & (2,526,950.003, 529,197.494) & (0.400, \(-\)0.093) & 0.411 \\ Test3 & (2,526,955.328, 529,197.345) & Map21 & (2,526,954.959, 529,197.237) & (0.369, 0.108) & 0.384 \\ Test4 & (2,526,962.757, 529,197.288) & Map27 & (2,526,963.033, 529,197.179) & (\(-\)0.276, 0.109) & 0.297 \\ Test5 & (2,526,964.728, 529,200.288) & Map40 & (2,526,964.317, 529,200.125) & (0.411, 0.163) & 0.442 \\ Test6 & (2,526,961.826, 529,195.117) & Map25 & (2,526,962.651, 529,194.450) & (\(-\)0.825, 0.667) & 1.061 \\ Test7 & (2,526,957.945, 529,200.695) & Map56 & (2,526,958.586, 529,200.175) & (\(-\)0.641, 0.520) & 0.825 \\ Test8 & (2,526,954.711, 529,195.219) & Map14 & (2,526,955.301, 529,195.683) & (\(-\)0.590, \(-\)0.464) & 0.751 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relocalization Function Test Results in Scene 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Actual Coordinates**} & \multicolumn{2}{c}{**Relocalization**} & \multicolumn{2}{c}{**Metrics**} \\ \hline
**Input** & **Measured Coordinates** & **Matching** & **Predicted Coordinates** & **Coordinate** & **Translation** \\
**Images** & **(N, E)** & **Image** & **(N, E)** & **Error** & **Error** \\ \hline Test1 & (2,526,492.846, 529,213.087) & Map87 & (2,526,492.263, 529,213.078) & (\(-\)0.582, \(-\)0.009) & 0.583 \\ Test2 & (2,526,494.824, 529,212.805) & Map05 & (2,526,494.824, 529,213.010) & (0.000, 0.205) & 0.205 \\ Test3 & (2,526,505.666, 529,212.576) & Map13 & (2,526,506.105, 529,212.897) & (0.439, 0.321) & 0.544 \\ Test4 & (2,526,509.387, 529,212.480) & Map15 & (2,526,509.351, 529,212.906) & (\(-\)0.036, 0.426) & 0.428 \\ Test5 & (2,526,524.473, 529,211.980) & Map25 & (2,526,524.934, 529,211.925) & (0.461, \(-\)0.055) & 0.464 \\ Test6 & (2,526,529.884, 529,202.476) & Map37 & (2,526,530.136, 529,202.393) & (0.252, \(-\)0.083) & 0.265 \\ Test7 & (2,526,512.270, 529,205.204) & Map61 & (2,526,512.405, 529,204.933) & (0.135, \(-\)0.271) & 0.303 \\ Test8 & (2,526,496.651, 529,203.441) & Map72 & (2,526,496.683, 529,203.215) & (0.032, \(-\)0.226) & 0.228 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relocalization Function Test Results in Scene 2.  
conditions impose greater demands on the robustness of relocalization algorithms. To evaluate the performance of the proposed method under such circumstances, a comprehensive indoor dataset was collected using a remote-controlled vehicle, following a strategy similar to that used in outdoor scenarios. Two trajectories that did not overlap with the mapping path were selected as the test set. The acquired image data were fed into ORB-SLAM3 to generate camera trajectories, which were treated as ground truth for evaluation. The keyframes used during map construction in ORB-SLAM3 served as the map set, while the keyframes obtained during relocalization formed the test set. As shown in Figure 12, the blue trajectory represents the map keyframes, and the red and green trajectories correspond to the keyframes from the two relocalization trials. After data collection was completed, the proposed relocalization method was applied to evaluate the performance on the test set.