# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 4 Experiment
### Overall Performance  
Aiming to prevent the impact of experimental hardware and its computing power, results from ORB-SLAM2 and ORB-SLAM3 [12] experiments are obtained by running under the experimental equipment. We evaluate the average tracking time of different methods for each frame on 9 sequences of the KITTI dataset. Table 1 shows the experiment outcomes, with each number gathered after 10 runs and averages. It is clear that the algorithm proposed in this study achieves an advantageous effect in time and accuracy. Compared with ORB-SLAM2, we save a lot of time without losing accuracy. Compared with  
Figure 6: This graph shows the comparison of the feature extraction outcomes. (**a**,**c**,**e**) are the results of the features extracted using the algorithm of this paper in the KITTI dataset, in the TUM fr1 sequence, and in the EuRoC MH01 sequence, respectively. (**b**,**d**,**f**) are the outcomes of features extracted applying the conventional ORB algorithm in the KITTI dataset, in the TUM fr1 sequence, and in the EuRoC MH01 sequence, respectively. ORB-SLAM3, in most sequences, SLAM using this algorithm achieves better performance. Some estimated trajectories are shown in Figure 7. However, the difference in trajectories between the two algorithms is relatively small due to the large size of the real trajectories, so we cannot see a significant difference in this figure. The speed of feature extraction has a significant impact on how well SLAM performs in real time. We conducted experiments on four sequences of fr1 in the Tum dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ORB-SLAM 2**} & \multicolumn{2}{c}{**ORB-SLAM 3**} & \multicolumn{2}{c}{**Our Method**} \\
**Sequence** & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**RMSE (m)**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**RMSE (m)**} & \multicolumn{2}{c}{**Mean**} \\  & **Time (ms)** & **RMSE (ms)** & **Time (ms)** & **Time (ms)** & **Time (ms)** & **RMSE (m)** \\ \hline
00 & 58.388 & 1.293 & 36.161 & 1.242 & **35.607** & **1.224** \\
01 & 80.643 & **10.422** & 31.035 & 14.755 & **30.637** & 13.926 \\
02 & 62.803 & 6.095 & **35.727** & 5.967 & 35.907 & **5.422** \\
03 & 62.404 & **0.670** & 31.703 & 1.326 & **31.254** & 1.309 \\
04 & 64.720 & 0.226 & **32.114** & 0.219 & 32.143 & **0.206** \\
05 & 65.096 & **0.793** & 34.178 & 0.967 & **33.638** & 0.817 \\
06 & 70.992 & **0.770** & **35.875** & 1.231 & 36.2021 & 0.945 \\
07 & 61.913 & 0.539 & **39.554** & 0.483 & 42.281 & **0.450** \\
09 & 62.606 & 3.056 & 32.568 & 2.049 & **32.5301** & **1.989** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing the performance of the three methods on the KITTI dataset with respect to mean tracking time and the RMSE of ATE. Figure 7: Comparison of trajectories on the KITTI datasets, where (**a**–**d**) are results on sequences 00, 02, 05, and 07, respectively. The black line represents ground truth. The feature extraction time of the method and ORB-SLAM3 is displayed in Table 2. Table 3 provides the RMSE of the two algorithms' ATE. In order to avoid being affected by accidental results when evaluating the performance of each approach, we run each algorithm 10 times and average the results to obtain two tables of data. During the experiment, ORB-SLAM3 [12] has experienced multiple local map tracking failures when running on the fr1_desk sequence, which has an impact on the accuracy of positioning and mapping. Our method does not have such phenomena in repeated operations and has certain robustness. From Tables 2 and 3, we can see that the suggested algorithm reduces the time of feature extraction to a certain extent, which is beneficial to the subsequent camera tracking and improves the robustness of SLAM. Figure 8 is the trajectory diagram of two of the sequences. It can be seen that the camera motion predicted by our SLAM algorithm is closer to the ground-truth motion trajectory than that obtained by ORB-SLAM3 in the presence of handheld device shaking.