# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 4 Experiment
### VO Module Comparison
By comparing the accuracy values, the VO accuracy in this paper is better than the original ORB-SLAM2 and can achieve similar accuracy to, or even higher accuracy than, ORB-SLAM3; the main reason is that the VO will perform a preoptimization after extracting the keyframe feature points, and then the back end will perform nonlinear optimization to achieve higher accuracy. For the Handheld Camera dataset, the VO in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Name** & **ORB SLAM2** & **ORB SLAM3** & **Ours** \\ \hline fr1/desk & 39.211 & 33.819 & 30.904 \\ fr1/desk2 & 39.207 & 33.534 & 29.392 \\ fr1/room & 34.941 & 30.634 & 26.732 \\ fr2/desk & 34.408 & 30.125 & 26.109 \\ fr2/slam & 27.191 & - & 20.781 \\ fr3/office & 37.443 & 32.903 & 25.847 \\ fr3/stn & 25.932 & 22.289 & 19.049 \\ fr3/rpy & 40.540 & 37.073 & 30.372 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison experiments on the processing time per frame of the three algorithms in the front end of the VO module (ten experiments to take the average value, in milliseconds). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Name** & **ORB SLAM2** & **ORB SLAM3** & **Ours** \\ \hline fr1/desk & 90\% & 100\% & 100\% \\ fr1/desk2 & 90\% & 100\% & 90\% \\ fr1/room & 100\% & 100\% & 100\% \\ fr2/desk & 100\% & 100\% & 100\% \\ fr2/slam & 20\% & - & 30\% \\ fr3/office & 100\% & 100\% & 100\% \\ fr3/stn & 100\% & 100\% & 100\% \\ fr3/rpy & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Three algorithm stability experiments (number of successes in ten experiments). this paper has the highest accuracy among the two datasets, fr1/desk and fr1/desk2, in which the camera moves faster, and this high accuracy is due to a fast frame-tracking VO module. ORB-SLAM3 has the highest accuracy on fr1/room, which is affected by the ambient illumination of this dataset, resulting in inaccurate pixel gray extracted by this algorithm. In two datasets, fr2/desk and fr3/stn, where the camera moves slowly, the VO in this paper is significantly superior to the original ORB-SLAM2 in accuracy, and is similar to ORB-SLAM3. In a home-environment-like dataset fr3/office, the camera is rotated along its environment for map construction, which is a test of the algorithm's loop closure performance. Fortunately, all three algorithms are able to perform accurate loop closure, but in this paper, VO is able to obtain a better accuracy due to the optimization of the pose using more information of keyframes. For the Robot dataset, the spatial jump amplitude of the fr2/slam dataset is relatively small, and the presence of debris on the ground causes the robot to shake, which results in transient tracking loss of the camera, and the subsequent tracking process is quite stable. This Robot dataset is only used for reference purposes in this paper due to their own data loss problems, and their accuracy is not judged. For the Dynamic Objects dataset, the fr3/rpy dataset will inevitably generate the feature points on the human body in 3D space in the form of map point while the camera is moving, and thus the camera will make misjudments when solving the pose, resulting in the reduction of pose accuracy. The method in this paper can reduce the extraction of feature points on dynamic objects as a way to reduce misjudgment in pose estimation. In the case of a satisfactory accuracy result, this paper is also interested in the degree of dispersion of the trajectory error of this VO module, so the performance of the three algorithms in eight datasets is counted separately, as shown in Table 5 of the STD comparison of the three algorithms. The experimental results show that the VO module in this paper has the smallest STD between the trajectory estimates and the ground truth means in the seven datasets, reflecting the excellent precision of the module. For the Handheld Camera dataset, combined with Table 4 for analysis, the RMSE effect of the VO module on fr1/room in this paper is not ideal,