## 4 Discussion  
In this study, we propose an enhanced visual SLAM method based on the principal direction projection of feature points, improving the applicability and computational \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline  & **Sequences** & **03** & **04** & **05** & **06** & **07** & **Mean** \\ \hline \multirow{3}{*}{Pose pred} & ORB SLAM3 & 1.924 & 1.653 & 1.868 & 1.770 & 1.833 & 1.8096 \\  & ORB-LINE-SLAM & 20.311 & 15.015 & 18.865 & 16.424 & 20.987 & 18.320 \\  & Proposed method & 2.925 & 2.247 & 2.950 & 2.655 & 2.754 & 2.7062 \\ \hline \multirow{3}{*}{Total tracking} & ORB SLAM3 & 92.991 & 107.006 & 109.367 & 105.990 & 106.371 & 104.345 \\  & ORB-LINE-SLAM & 345.445 & 347.838 & 359.972 & 347.946 & 358.183 & 351.877 \\ \cline{1-1}  & Proposed method & 73.294 & 81.968 & 81.852 & 79.478 & 78.274 & 78.973 \\ \cline{1-1}  & Reduction & 21.187\%/78.78\% & 23.47\%/76.44\% & 25.16\%/77.26\% & 26.41\%/77.16\% & 24.23\%/78.15\% & 24.23\%/77.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Running time of pose prediction and total tracking (in _ms_). Experimental sequences are selected from 03 to 07, from the KITTI dataset. The ratios in the reduction represent ORB SLAM and ORB-LINE-SLAM. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Seq.** & **Length** & **LC** & **ORB SLAM3** & **Proposed Method** \\ \hline \multirow{4}{*}{KITTI} & 00 & 3724 & \(\surd\) & 4.112/1.211 & 3.299/1.286 \\  & 01 & 2453 & \(-\) & 24.986/15.027 & 23.617/14.541 \\  & 04 & 394 & \(-\) & 0.422/0.241 & 0.388/0.217 \\  & 05 & 2206 & \(\surd\) & 1.688/0.957 & 1.680/0.918 \\ \hline \multirow{4}{*}{EuRoC} & MH01 & 81 & \(-\) & 0.161/0.045 & 0.137/0.044 \\  & MH02 & 74 & \(-\) & 0.103/0.044 & 0.103/0.045 \\  & MH03 & 131 & \(-\) & 0.122/0.056 & 0.134/0.048 \\  & MH04 & 92 & \(-\) & 0.209/0.057 & 0.217/0.069 \\ \hline \multirow{4}{*}{TUM} & room1 & 146 & \(\surd\) & 0.128/0.078 & 0.127/0.076 \\  & room2 & 142 & \(\surd\) & 0.130/0.050 & 0.130/0.051 \\ \cline{1-1}  & corridor1 & 305 & \(\surd\) & 0.257/0.107 & 0.215/0.092 \\ \cline{1-1}  & corridor2 & 322 & \(\surd\) & 0.295/0.119 & 0.252/0.104 \\ \hline \hline \end{tabular}  
* Seq.: the experimental sequences from dataset. Length: the length of trajectory in \(m\). LC: loop closure. \(\surd\): provide loop closure.  
\end{table} 
Table 7: Average localization error (Max/RMSE in _m_) over five runs of the executions. efficiency. We also integrate global image information, grayscale, and gradient direction to construct feature descriptors for position recognition. Experimental results show that introducing principal direction projection error for position estimation accuracy can achieve satisfactory gains with the same number of levels. Using this method while reducing the number of feature extraction levels improves computational efficiency while maintaining positioning accuracy. The proposed loop-closure-detection method improves detection efficiency and accuracy compared to the comparison method. In this discussion, we will analyze the benefits of the method in terms of detection efficiency, detection accuracy, and localization accuracy. The second section of the experimental study evaluates the computational efficiency of the proposed loop-detection method by comparing it with BOW2 [38]. To improve computational efficiency, the proposed method optimizes two aspects. First, it reduces the dimensionality of the data by aggregating the 2D feature matrix into a 1D feature vector, reducing storage space and decreasing the complexity of distance calculations, leading to higher efficiency. Second, a multi-layer detection strategy is employed, where the first-layer-detection phase prioritizes speed by using a simple distance calculation to identify candidate frames, and the second-layer-detection phase focuses on accuracy, thereby improving the overall detection efficiency.