# NGLSFusion: Non-Use GPU Lightweight Indoor Semantic SLAM
## 4 Experiment
### VO Module Comparison  
The experiments in this section select sequences from eight TUM RGB-D datasets for validation, which contain the various complex environments described above. The average time-consumption of ORB-SLAM2 [13], ORB-SLAM3 [14], and the algorithm in this paper were compared by calculating the feature point extraction time-consumption per frame, in which the dataset of fr2/slam, a mobile robot viewpoint, has spatial jumps, which led to visualization jams in ORB-SLAM3, so these data were not used. The average time-consumption for feature point extraction is shown in Table 1.  
The experimental results show that it is nearly twice as fast as the original ORB-SLAM2 in terms of feature point extraction per frame. ORB-SLAM2 uses ORB features for front-end feature point extraction, on the basis of which ORB-SLAM3 changes the original static matrix to feature matrix, and the speed is significantly improved. The results also show that the combination of ORB features, optical flow method, and direct method used in this paper to process image frames is far superior to ORB-SLAM2 and ORB-SLAM3 in terms of speed. The following is a comparison of the average time-consumption per frame by the three algorithms at the front end, as shown in Table 2 of the average front-end time-consumption. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Name** & **ORB SLAM2** & **ORB SLAM3** & **Ours** \\ \hline fr1/desk & 18.454 & 17.177 & 9.856 \\ fr1/desk2 & 18.660 & 17.072 & 9.479 \\ fr1/room & 17.088 & 15.982 & 9.342 \\ fr2/desk & 16.555 & 15.241 & 9.854 \\ fr2/slam & 15.573 & - & 7.979 \\ fr3/office & 16.621 & 15.910 & 8.418 \\ fr3/stn & 12.653 & 11.915 & 6.923 \\ fr3/rpy & 21.366 & 19.138 & 11.508 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison test of the three algorithms in terms of feature point extraction speed per frame (ten experiments are taken as the average value, in milliseconds). The experimental results show that the proposed feature point extraction method leads to a speedup in the overall VO front-end time consumption. In the VO module front end, feature point extraction and matching occupy a large amount of time. In this paper, the VO module is improved from feature point extraction, and although redundant calculations are generated when processing non-keyframes, in terms of the VO module as a whole, this paper provides a lightweight VO that can estimate the pose faster. The faster the speed of VO, the more frames are tracked, allowing VO to track more accurately in fast-moving or rotating datasets. Of course, this paper pays more attention to the stability of the algorithm, and counts the success rates of the three algorithms running datasets, as shown in Table 3.  
The experimental results show that the original ORB-SLAM2 algorithm is prone to run failure in datasets where the camera moves too fast and dynamic objects are present, while the ORB-SLAM3 algorithm completes the whole process by rebuilding the map when the tracking fails because of the presence of a multimap system. However, the algorithm in this paper does not need obvious texture features in the tracking process, and it can only track the gray changes in the image, which fully reflects its stability. The accuracies of the three algorithms are further compared while satisfying a rapidity and stability. The ORB-SLAM2 and ORB-SLAM3 gave some results processed by double standard deviation in their papers. Considering the influence of different devices on the algorithm results, the results given in this paper meet the requirements of the original paper after testing, as shown in the RMSE comparison of the three algorithm in Table 4.  
The experimental results show that the VO module in this paper has the highest accuracy on seven datasets, and ORB-SLAM3 has the highest accuracy on only one dataset, respectively.