## 4 Experiment and Analysis
### Indoor Posture Estimation Experiment  
This paper develops a set of information determination methods to retain the static feature points in the dynamic detection frame. A feature point is considered dynamic only if it is in the dynamic detection frame and not in the static detection frame. Rejecting a feature point based solely on its location will affect the accuracy of the system. Figure 8a illustrates the effect of culling, while Figure 8b displays the image prior to dynamic feature point culling and Figure 8b displays the image subsequent to culling. The man's plaid shirt in the figure has a lot of feature points that, if left unculled, will significantly lower the system's accuracy. This paper's method successfully identifies the dynamic objects in the image and retains the feature points on the static objects, like computers, while rejecting the feature points on the dynamic objects. Robustness in SLAM refers to the system's capacity to withstand a range of errors and disturbances. The accuracy of the system will be affected by the presence of dynamic objects in the dataset, but the system's robustness will be demonstrated by its equally good performance in managing the dynamic dataset. The difference between the calculated and true values of the posture is represented by the Absolute Posture Error (APE), which provides a visual evaluation of the algorithm's correctness. The difference in the amount of position change at the same timestamp is represented by the Relative Posture Error (RPE), which is a useful tool for estimating system drift. After aligning the estimated and actual position values using the timestamps, the change in position is computed between the true and estimated values over time. The difference in the change in values represents the position error of the camera. Figure 8: Dynamic feature point culling effect diagram. (**a**) Before culling, (**b**) after culling. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & **Parameter Quantity** & **Calculation Quantity** & **Detection Time (ms)** \\  & **(10\({}^{6}\) M)** & **(GFLOPs)** & **Detection Time (ms)** \\ \hline YOLOv51 & 45.65 & 110.6 & 29.65 \\ YOLOv5m & 22.15 & 53.6 & 27.51 \\ YOLOv5s & 7.12 & 18.2 & 24.83 \\ YOLOv5s- & 3.50 & 6.8 & 22.17 \\ MobileNetV3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of experimental results of different models. Tables 4 and 5 present comparative test results between ORB-SLAM3 and the algorithm proposed in this paper across three different datasets. The evaluation metrics include root-mean-square error (RMSE), mean error (MEAN), and standard deviation (SD). The improvement ratio is calculated using the formula given in Equation (7):  
\[Improvement=\frac{m-n}{m}\times 100\% \tag{7}\]  
where \(m\) is the running result of ORB-SLAM3 and \(n\) is the running result of the algorithm in this paper. From Table 5, it can be seen that the algorithm in this paper has improved the position estimation accuracy compared to ORB-SLAM3 after the dynamic feature points are removed. In terms of absolute trajectory error, the root-mean-square error in the walking_xyz sequence is improved by 80.02%, the mean error is improved by 92.09%, and the standard deviation is improved by 87.92%. In terms of the relative displacement trajectory error, the root-mean-square error in the walking_xyz sequence is improved by 71.71%, the mean error is improved by 82.65%, the standard deviation is improved by 61.58%, and the other two dynamic sequences are also significantly improved. Figure (a)a shows the camera trajectory of ORB-SLAM3 in the walking_xyz sequence, and Figure (b)b shows the camera trajectory of our SLAM in the walking_xyz sequence, where the solid line is the trajectory value computed by the system and the dashed line is the real trajectory value of the camera. Figure (c)c shows the comparison between ORB-SLAM3 and our SLAM in xyz mode with the real trajectory, and Figure (d)d shows the comparison between ORB-SLAM3 and our SLAM in rpy mode with the real trajectory.