# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 4 Experiment
### VO Module Comparison
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Name** & **ORB SLAM2** & **ORB SLAM3** & **Ours** \\ \hline fr1/desk & 0.017280 & 0.017063 & 0.016755 \\ fr1/desk2 & 0.026217 & 0.025845 & 0.025596 \\ fr1/room & 0.059723 & 0.055109 & 0.096578 \\ fr2/desk & 0.008506 & 0.007727 & 0.007046 \\ fr2/slam & 0.109209 & - & 0.075307 \\ fr3/office & 0.009770 & 0.009472 & 0.008647 \\ fr3/stn & 0.010764 & 0.010350 & 0.010117 \\ fr3/rpy & 1.149160 & 0.772202 & 0.645489 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RMSE of the three algorithms (median value after 10 tests, in meters). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Name** & **ORB SLAM2** & **ORB SLAM3** & **Ours** \\ \hline fr1/desk & 0.009794 & 0.010109 & 0.008273 \\ fr1/desk2 & 0.011452 & 0.011167 & 0.008016 \\ fr1/room & 0.034836 & 0.028755 & 0.022864 \\ fr2/desk & 0.003302 & 0.003041 & 0.002659 \\ fr2/slam & 0.048128 & - & 0.027429 \\ fr3/office & 0.004309 & 0.004108 & 0.003326 \\ fr3/stn & 0.003955 & 0.005600 & 0.003475 \\ fr3/rpy & 0.560861 & 0.338517 & 0.350338 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: STD of three algorithms (median value after 10 tests, in meters). but its STD is the smallest, and it can be concluded that the camera trajectory estimation does not show substantial fluctuations compared to ground truth. This reason is mainly attributed to the use of the PnP-Ransac algorithm for preoptimization of camera pose and nonlinear optimization of the back end in this paper, which makes the camera trajectory estimation error smaller. For the Robot dataset, the use of RMSE and STD metrics is still not convincing, so the results are only for reference. For the Dynamic Objects dataset, the STD of VO in this paper is smaller, even half of the original ORB-SLAM2. Considering that the trajectory estimation of the algorithm in this paper cannot be visualized from the data perspective, the trajectory is depicted on the basis of the data in Tables 4 and 5, as shown in Figures 6 and 7 for the comparison of trajectory pose. Among them, Figure 6a-c,g,i,j are the top view of the trajectory, and Figure 6d-f,h,k,l are the side view of the trajectory. The experimental results show that the VO module in this paper achieved the expected effect in estimating the camera pose. In Figure 6, by testing the performance of the module on six datasets and comparing the trajectories with the original ORB-SLAM2 and ORB-SLAM3, it is found that the module far exceeds the original ORB-SLAM2 in terms of estimated pose accuracy and is similar to ORB-SLAM3 in terms of accuracy, and is even better in terms of pose accuracy for some trajectories, which is closer to the ground truth. In this dataset, for the camera pose estimated by the VO module of this paper in the trajectory maps of (a, d) and (i, k), two viewpoints fit the ground truth more closely than the other two algorithms. The camera poses in (b, e), (c, f), and (j, l) in both views show a certain magnitude of trajectory jumps, while the trajectory estimated by VO in this paper is closer to the ground truth, which shows the precision of this module. The camera poses estimated in (g, h) views show substantial trajectory jumps, but the VO in this paper can track a large number of frames and obtain more information about the pose, so the camera poses estimated by VO in this paper are the best in the trajectory. Figure 6: _Cont._  
Among them, Figure 7a,c are the top view of the trajectory, and Figure 7b,d are the side view of the trajectory. In the Robot dataset, (a, b) cannot provide an accurate basis for the VO in this paper, but it can verify the robustness of the VO in this paper on the mobile robot platform theoretically and practically through RMSE, STD, and trajectory pose plots, respectively.