# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 4 Experiments and Evaluation
### KITTI Dataset Evaluation  
We tested the proposed algorithm on the KITTI dataset [36]. The quantitative evaluations for the different SLAM systems were the absolute trajectory error (ATE) [40] and the relative pose error (RPE) [41] based on translations and rotations. Table 1 shows the performance of this system was better than ORB-SLAM2, especially in sequences with strong lighting, motion blur, and low texture areas, such as 06 and 09. It can be seen that the multifeature fusion not only improved the accuracy of the algorithm but also avoided the degradation problem that may occur in the pose solution algorithm when using a single feature.  
Figure 8 shows the comparison results of ORB-SLAM2 and the method in this paper on KITTI's partial sequences. It can be seen that the algorithm of this paper was equivalent to ORB-SLAM2 as a whole. However, in sequences such as 00, 02, and 09, it showed that our method had the best results. On the other hand, the ORB-SLAM2 lost track easily and did not have a whole trajectory.  
Figure 9 depicts the variation curve of the pose estimation error with the number of training iterations. When we used more than 80 iterations, the rotation and translation errors were relatively small while the performance improved, which showed that the algorithm in this paper had a good convergence.  
Figure 10 visualizes the statistical error property between our system and ORB-SLAM2 on sequence 09 of KITTI. It shows that our model achieved obviously a better performance than ORB-SLAM2 in terms of the root-mean-square error (controlled within 2 m), extreme value error, and the sum of the squared errors. Figure 10 shows the columnar statistical comparison of postural errors; our model was better than ORB-SLAM2 in the APE distribution range of the algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Seq**} & \multicolumn{4}{c}{**ORB-SLAM2**} & \multicolumn{4}{c}{**Our**} \\ \cline{2-7}  & **ATE (m)** & \(\mathbf{RPE_{trans}}\) **(\%)** & \(\mathbf{RPE_{rot}}\) **(deg/m)** & **ATE (m)** & \(\mathbf{RPE_{trans}}\) **(\%)** & \(\mathbf{RPE_{rot}}\) **(deg/m)** \\ \hline
00 & 1.266 & 52.5 & 0.363 & 1.233 & 2.9 & 0.122 \\
01 & 4.296 & 3.4 & 0.420 & 2.616 & 4.8 & 0.044 \\
02 & 12.790 & 4.3 & 0.107 & 12.721 & 3.6 & 0.077 \\
03 & 0.403 & 0.8 & 0.072 & 0.385 & 2.0 & 0.055 \\
04 & 0.466 & 2.2 & 0.055 & 0.192 & 2.1 & 0.040 \\
05 & 0.348 & 2.3 & 0.144 & 0.402 & 1.7 & 0.056 \\
06 & 1.184 & 3.9 & 0.089 & 0.572 & 1.8 & 0.042 \\
07 & 0.439 & 1.3 & 0.076 & 0.436 & 1.6 & 0.046 \\
08 & 3.122 & 12.1 & 0.076 & 2.874 & 3.9 & 0.054 \\
09 & 3.319 & 15.0 & 0.104 & 1.537 & 2.2 & 0.054 \\
10 & 0.927 & 2.6 & 0.090 & 0.989 & 2.1 & 0.060 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison between ATE and RPE on different SLAM algorithms.  
Figure 8: Comparison of trajectories estimated by our SLAM method, ORB-SLAM2, and the ground truth on the KITTI dataset.  
Figure 9: The variation curve of the pose estimation error with the number of training iterations on sequence 09 of KITTI. Each color represents a different number of sampling points.