# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 4 Experiments and Evaluation
### Ablation Study  
We used two datasets to demonstrate our proposed knowledge distillation. The result of the teacher network and the student network are shown in Figure 15. Compared with the true value, both models could identify key points and line segments with high precision. Although there were some small missing line segments and connection errors in the results of the student network, the expression of the line-segment structure in the environment was basically accurate. The quantitative comparison is shown in Table 2. Although the performance of the student network was slightly lower than that of the teacher network, the operation speed was increased by 73%.  
To verify the role of the multifeature fusion in the SLAM system, the root-mean-square error (RMSE) of the ATE index under different feature combinations was calculated. The experimental results are given in Table 3, where the point-line feature combination method used in this paper significantly improved the accuracy of the pose estimation. To evaluate our design decisions, we evaluated four different variants with results. This ablation study, presented in Table 4, showed that all HAGNN blocks were useful and brought substantial performance gains. \"No EAGAT\" replaced all EAGAT layers with CHGI layers, and the matching accuracy decreased by 9.7%. \"No CHGI\" replaced all CHGI layers with EAGAT layer, and the precision of the resulting matching decreased by 22.6%, \"No HAGNN\" replaced the graph neural network with a single linear projection, and the precision of the resulting matching decreased by 26.1%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Wireframe dataset**} & \multicolumn{3}{c}{**YorkUrban dataset**} & \multirow{2}{*}{**FPS**} \\ \cline{2-2} \cline{5-8}  & \(F^{H}\) & \(sAP\) & \(LAP\) & \(F^{H}\) & \(sAP\) & \(LAP\) & \\ \hline Student & 77.5 & 58.9 & 59.8 & 64.6 & 25.9 & 32.0 & 12.5 \\ Teacher & 80.6 & 57.6 & 61.3 & 67.2 & 27.6 & 34.3 & 7.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative evaluation of PL-Net point-line detection knowledge distillation method on Wireframe dataset and YorkUrban dataset.  
Figure 14: The difference graph of the Wasserstein distance. (**a**) GIPOT under different conditions and convergence trajectory graph. We also plotted the ones for the Sinkhorn method for comparison. (**b**) The average time of GIPOT and Sinkhorn iterations under different conditions.  
The proposed HAGNN was compared with two feature matching methods: the nearest neighbor (NN) method and SuperGlue. As show in Table 5, it can be seen clearly that HAGNN had a significantly higher pose estimation accuracy than all competitors, which showed a higher feature expression ability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Known**} & \multicolumn{2}{c}{**Unknown**} \\ \cline{2-5}  & **Match Precision** & **Matching Score** & **Match Precision** & **Matching Score** \\ \hline No EAGAT & 79.6 & 29.5 & 55.3 & 15.6 \\ No CHGI & 66.7 & 25.3 & 48.2 & 18.5 \\ No HAGNN & 63.2 & 19.4 & 51.2 & 10.3 \\ Full & 89.3 & 34.2 & 78.3 & 23.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation of HAGNN.  
Figure 15: Qualitative evaluation of PL-Net point–line detection knowledge distillation method on the Wireframe dataset and the YorkUrban dataset.