## 2 Materials and Methods
### Trajectory Accuracy
When the number of levels and factor are 8 and 1.2, respectively, the image at the highest level will be less than \(1/12\) of the original (with level 0 as the starting level), within a reasonable range. Considering the above discussion, we choose four levels as the final experimental number of levels to balance efficiency and accuracy, with a factor of 1.54, reducing the area of the top image to about \(1/12\) of the original. The primary improvement proposed in this method is in the tracking component, so the focus is on comparing the computational efficiency gains during tracking, including the time taken for pose prediction and the overall tracking process. The test sequences selected are sequences 03, 04, 05, 06, and 07 from the KITTI dataset, with loop closures in sequences 03 and 04. The experimental results are shown in Table 6. The results indicate that reducing the number of levels in the feature pyramid can yield a 24.23% improvement in efficiency compared to ORB SLAM3. The feature-extraction process operates at the pixel level and involves grid partitioning, with the extraction time related to the image size. Level 0 has the most prominent image size and, thus, the highest time cost. Adjusting the number of levels aligns with theoretical expectations, bringing the expected benefits. ORB-LINE-SLAM does  
Figure 13: Comparison of trajectory results. The red box indicates a locally magnified trajectory. The left and right images correspond to sequences 05 and 06, both from the KITTI dataset. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & **Sequences** & **03** & **04** & **05** & **06** & **07** \\ \hline \multirow{4}{*}{RMSE} & ORB SLAM3 & 1.3514 & 0.2406 & 2.1088 & 2.1038 & 1.2341 \\  & ORB-LINE-SLAM & 1.2060 & 0.2367 & 1.9920 & 1.9270 & 1.3885 \\  & Proposed method & 1.3210 & 0.2172 & 1.8426 & 1.6187 & 1.1642 \\  & Reduction & \(2.25\%/-9.54\%\) & \(9.76\%/8.24\%\) & \(12.62\%/7.50\%\) & \(23.06\%/16.00\%\) & \(5.67\%/16.15\%\) \\ \hline \multirow{4}{*}{Max} & ORB SLAM3 & 2.4148 & 0.4221 & 5.1520 & 4.0300 & 2.8769 \\  & ORB-LINE-SLAM & 2.0625 & 0.4563 & 5.1518 & 3.9385 & 3.2673 \\ \cline{1-1}  & Proposed method & \(2.3555\) & 0.3876 & 3.9905 & 3.0479 & 2.6627 \\ \cline{1-1}  & Reduction & \(2.45\%/-14.21\) & \(8.16\%/15.06\%\) & \(22.55\%/22.54\%\) & \(24.37\%/22.61\%\) & \(7.45\%/18.51\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average localization error (RMSE and Max in \(m\)) over five runs of the executions without loop correction. Experimental sequences are selected from 03 to 07, from the KITTI dataset. The ratios in the reduction represent ORB SLAM and ORB-LINE-SLAM. not demonstrate an advantage in terms of temporal efficiency. The maximum tracking time reaches 359 ms, indicating that integrating line features imposes a significant computational burden on the system. Additionally, incorporating principal direction information into pose estimation increased the computation time by approximately 1 _ms_. Comprehensive tests are conducted to verify the positioning accuracy of the improved visual SLAM. The datasets chosen are KITTI, EuRoC, and TUM, with detailed descriptions of the dataset scenarios in Table 1. Pose estimation uses the improved pose estimation method. The loop-closure-detection method uses the one proposed in our work, with geometric verification also incorporated after loop-closure detection to prevent false detections. The comparison method ORB SLAM3 uses BOW2 for loop-closure detection with default parameters. The integration of line features significantly reduces the real-time performance of ORB-LINE-SLAM, which is unacceptable for practical use. Therefore, in the comprehensive experiments, we only use ORB-SLAM3 as the comparison method. The experimental results are shown in Table 7. The results demonstrate that the improved system does not decrease the positioning accuracy and even brings slight gains in most sequences.