## 5 Experimental Results
### Results and Discussions  
The results of estimating VO/camera pose/camera trajectory using the TQU-SLAM benchmark dataset when using SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, RAZE [18], BRISK, and VGG to extract features are shown in Table 2. The average distance error of the ORB2 feature is the lowest (\(Err_{d}\) = 5.74 mm). The ratio of detected and extracted frames characterized for 6-DOF camera pose estimation is shown in Table 3. The average ratio of detected frames with the SHI_TOMASI feature is the highest (\(r_{d}=98.97\%\)). Figure 11 shows the results of estimating the VO of FO-D of the TQU-SLAM benchmark dataset when using the SHI_TOMASI features extracted from RGB images. Figure 11 also shows that SHI_TOMASI features are extracted better and more in the first FO-D compared to data at other times. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset/Methods**} & \multirow{3}{*}{**Features**} & \multicolumn{6}{c}{**Distance Error (\(Err_{d}\) (mm)) of the TQU-SLAM Benchmark Dataset**} \\ \cline{3-10}  & & \multicolumn{2}{c}{**1 st**} & \multicolumn{2}{c}{**2 nd**} & \multicolumn{2}{c}{**3 rd**} & \multicolumn{2}{c}{**4 th**} \\ \cline{3-10}  & & **FO-D** & **OP-D** & **FO-D** & **OP-D** & **FO-D** & **OP-D** & **FO-D** & **OP-D** \\ \hline \multirow{6}{*}{PySLAM} & SHI\_TOMASI & 6.019 & 2.903 & 5.938 & 6.836 & 14.42 & 10.978 & 8.224 & 3.050 \\ \cline{2-10}  & SIFT & 38.93 & 13.02 & 37.63 & 13.58 & 23.55 & 1.63 & 32.02 & 2.11 \\ \cline{2-10}  & SURF & 39.26 & 13.59 & 38.07 & 13.57 & 27.73 & 38.81 & 38.98 & 14.29 \\ \cline{2-10}  & ORB & 1.42 & 14.67 & 32.48 & 12.19 & 25.93 & 13.29 & 0.75 & 2.07 \\ \cline{2-10}  & ORB2 & 8.42 & 10.14 & 7.54 & 9.16 & 8.54 & 2.94 & 8.47 & 3.10 \\ \cline{2-10}  & AKAZE & 40.34 & 6.72 & 37.75 & 12.67 & 30.60 & 6.78 & 37.15 & 2.11 \\ \cline{2-10}  & KAZE & 38.32 & 15.55 & 31.30 & 12.45 & 15.89 & 30.87 & 41.66 & 17.22 \\ \cline{2-10}  & BRISK & 1.62 & 14.38 & 28.87 & 13.56 & 12.43 & 1.67 & 9.54 & 2.16 \\ \cline{2-10}  & VGG & 11.67 & 11.66 & 11.66 & 11.67 & 0.65 & 11.66 & 0.75 & 2.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of estimating VO on the TQU-SLAM benchmark dataset when using the extracted features: SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, BRISK, and VGG. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Dataset/Methods**} & \multirow{3}{*}{**Features**} & \multicolumn{6}{c}{**Aspect Ratio Fails to Detect Keypoints of the TQU-SLAM Benchmark Dataset (\(r_{d}\)(\%))**} \\ \cline{3-9}  & & \multicolumn{2}{c}{**1 st**} & \multicolumn{2}{c}{**2 nd**} & \multicolumn{2}{c}{**3 rd**} & \multicolumn{2}{c}{**4 th**} \\ \cline{3-9}  & & **FO-D** & **OP-D** & **FO-D** & **OP-D** & **FO-D** & **OP-D** & **FO-D** & **OP-D** \\ \hline \multirow{6}{*}{PySLAM} & SHI\_TOMASI & 99.17 & 99.02 & 99.72 & 99.85 & 98.97 & 98.97 & 97.96 & 98.09 \\ \cline{2-9}  & SIFT & 99.69 & 79.57 & 97.18 & 76.65 & 44.50 & 42.58 & 86.63 & 51.80 \\ \cline{2-9}  & SURF & 99.67 & 97.84 & 95.51 & 94.66 & 95.76 & 98.95 & 96.76 & 97.66 \\ \cline{2-9}  & ORB & 2.84 & 34.84 & 45.39 & 49.78 & 7.02 & 38.48 & 1.53 & 3.04 \\ \cline{2-9}  & ORB2 & 98.97 & 99.23 & 98.48 & 99.28 & 95.64 & 97.95 & 94.87 & 97.02 \\ \cline{2-9}  & AKAZE & 88.66 & 9.71 & 76.42 & 34.20 & 67.65 & 8.94 & 54.61 & 3.08 \\ \cline{2-9}  & KAZE & 90.36 & 25.51 & 75.83 & 46.15 & 89.29 & 9.04 & 76.36 & 19.20 \\ \cline{2-9}  & BRISK & 1.82 & 16.26 & 6.25 & 9.32 & 39.81 & 28.98 & 12.18 & 3.10 \\ \cline{2-9}  & VGG & 6.88 & 45.02 & 36.90 & 40.26 & 45.92 & 39.39 & 1.35 & 17.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The ratio of frames with detected features/keypoints in the TQU-SLAM benchmark dataset when using the following extracted features: SHI_TOMASI, SIFT, SURF, ORB, ORB2, AKAZE, KAZE, RAZE, RAZE, BRISK, and VGG. Figure 12 shows the results of estimating points on the moving trajectory on the 1 st-FO-D of the TQU-SLAM benchmark dataset when using the features extracted from VGG.