## 4 Experiment and Analysis
### Indoor Posture Estimation Experiment
From the figure, it can be seen that the trajectory calculated by ORB-SLAM3 has a large error, while the trajectory calculated by our SLAM is closer to the real value. Figure (a)a shows the results of the APE comparison between ORB-SLAM3 and our SLAM in the walking_xyz sequence, and Figure (a)a shows the results of the RPE comparison between ORB-SLAM3 and our SLAM in the walking_xyz sequence. Our SLAM performs significantly better than ORB-SLAM3 in APE and better than ORB-SLAM3 in RPE. It can be seen that the algorithm in this paper has less error in dynamic environments compared to ORB-SLAM3 and has higher accuracy and robustness. Table 6 displays the absolute trajectory errors of the algorithm used in this paper in comparison to the DS-SLAM and DynaSLAM algorithms. In the walking_xyz and walking_static sequences, Table 6 shows that the algorithm used in this study performs better than the other algorithms in terms of root-mean-square error, and that DS-SLAM and DynaSLAM are comparable in these two sequences. Meanwhile, in the walking_halfsphere \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**ORB-SLAM3 (m)**} & \multicolumn{3}{c}{**Our-SLAM (m)**} & \multicolumn{3}{c}{**Improvement (\%)**} \\ \cline{2-10}  & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** \\ \hline walking\_xyz & 0.6459 & 0.5122 & 0.4025 & 0.1290 & 0.0408 & 0.0486 & 80.02 & 92.09 & 87.92 \\ walking\_static & 0.3371 & 0.2107 & 0.2489 & 0.0365 & 0.0259 & 0.0285 & 89.17 & 87.70 & 88.54 \\ walking\_halfsphere & 0.4254 & 0.4022 & 0.3715 & 0.0801 & 0.0296 & 0.0653 & 81.17 & 92.64 & 82.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Absolute Posture Error comparison. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{3}{c}{**ORB-SLAM3 (m)**} & \multicolumn{3}{c}{**Our-SLAM (m)**} & \multicolumn{3}{c}{**Improvement (\%)**} \\ \cline{2-10}  & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** & **RMSE** & **MEAN** & **SD** \\ \hline walking\_xyz & 0.4256 & 0.3245 & 0.2356 & 0.1204 & 0.0563 & 0.0905 & 71.71 & 82.65 & 61.58 \\ walking\_static & 0.2245 & 0.0986 & 0.1856 & 0.0160 & 0.0120 & 0.0156 & 92.87 & 87.82 & 91.59 \\ walking\_halfsphere & 0.3661 & 0.2265 & 0.3103 & 0.0436 & 0.0520 & 0.0284 & 88.09 & 77.04 & 91.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Relative Posture Error comparison. sequence, DynaSLAM performs better than DS-SLAM with respect to standard deviation and root-mean-square error, while the technique presented in this research comes in second. Comparing the error of this work to similar methods over different dataset sequences, it performs reasonably well overall. Figure 10: APE and RPE results in walking_xyz sequence. (**a**) APE results, (**b**) RPE results. Figure 9: The results of APE in the walking_xyz sequence. (**a**) ORB-SLAM3 comparison results with true trajectories, (**b**) our SLAM comparison results with true trajectories, (**c**) comparison curves between the three trajectories in xyz mode, (**d**) comparison curves between the three trajectories in rpy mode. The comparison of the frame rate of this paper system with ORB-SLAM3, DS-SLAM, and DynaSLAM is presented in Table 8. The table shows that ORB-SLAM3 achieves the highest frame rate of 37 frames per second because it does not detect dynamic objects. When comparing DS-SLAM and DynaSLAM, the system described in this paper has the highest frame rate. This paper's system has the highest frame rate, able to sustain over 33 frames per second while the algorithm runs, demonstrating the improved real-time performance of the algorithm.