# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 4 Experiments and Evaluation
### Evaluation Using Real Environment  
In addition to the TUM RGB-D dataset, we also compared our algorithm with the open-source ORB-SLAM2 in a real environment. In the real environment, we add constantly moving pedestrians used to interfere with SLAM. The point cloud map of the dynamic environment constructed by ORB-SLAM2, and our algorithm is shown in Figures 12-14. Affected by the dynamic object, the pose estimation of the camera had large errors, as shown in Figure 13a. When the map was constructed, point cloud information was mapped \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**Relative Translation Error (m)**} & \multicolumn{3}{c}{**Relative Rotation Error (deg)**} \\ \cline{2-7}  & **ORB-SLAM2** & **DS-SLAM** & **Ours** & **Improvement** & **ORB-SLAM2** & **DS-SLAM** & **Ours** & **Improvement** \\ \hline f3\_s\_s & 0.009126 & **0.007644** & 0.007984 & \(-\)4.45\% & 0.007476 & 0.288099 & 0.276316 & \(-\)4.18\% \\ f3\_w\_h & 0.498807 & 0.031374 & **0.028132** & 10.33\% & 0.748307 & 10.440685 & **0.770315** & 5.66\% \\ f3\_w\_r & 0.584204 & 0.068926 & **0.050974** & 26.05\% & 0.817401 & 11.262848 & **1.071637** & 22.85\% \\ f3\_w\_s & 0.2267 & **0.008876** & 0.009831 & \(-\)10.76\% & 0.407255 & 4.104781 & **0.255871** & 1.20\% \\ f3\_w\_x & 0.404481 & 0.052734 & **0.020853** & 60.46\% & 0.823788 & 7.694351 & **0.638247** & 38.50\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relative pose error. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence** & **ORB-SLAM2** & **Ours** \\ \hline f3\_s\_s & 31.084 & 33.183 \\ f3\_w\_h & 23.159 & 31.861 \\ f3\_w\_r & 25.964 & 31.843 \\ f3\_w\_s & 22.362 & 28.167 \\ f3\_w\_x & 23.632 & 26.886 \\ Average & 25.240 & **30.388** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean tracking time [FPS] of ORB-SLAM2 and Ours in the TUM. to the wrong location. Meanwhile, the information of dynamic objects is also retained in the map, as shown in Figure 13. Compared to ORB-SLAM2, our algorithm greatly improves these drawbacks. In scenes with dynamic objects, our algorithm is able to estimate the pose and construct the map very well. In addition, the algorithm is also able to obtain semantic maps while perfectly removing dynamic objects, as shown in Figure 13a,b. Figure 11: Trajectory data visualization in different scenarios. The first row shows the results of trajectory data visualization in the freiburg3_walking_halfsphere scene, and the second row shows the results in thefreiburg3_walking_xyz scene. From the results, it is shown that the estimated trajectory of our algorithm deviates less from the real trajectory, and the maximum absolute trajectory error is only 0.0867 m when the sensor moves in halfsphere form and 0.056 m when it moves in xyz form. These results indicate that the accuracy of our algorithm’s bit pose estimation performs well in the high dynamic scenario. Figure 12: Point cloud map of dynamic scene. In terms of pose accuracy, our method is higher than the ORB-SLAM2 system in all scenes; the reason for this is that our system can well remove the feature points in the im-age which have movement, thus improving the matching accuracy between images. In addition, by jointly using dynamic target and area growing algorithms, we get more ac-curate locations of dynamic points in the depth image, thus ensuring that dynamic object features are less involved in the mapping process. The test results of real scenes show that the point cloud maps created by our system are more effective than ORB-SLAM2. Our system compares to DS-SLAM in terms of pose accuracy, we use homography combined with optical flow method to determine dynamic feature points, while DS-SLAM uses fundamental matrix combined with polar line constraints. Homography and fundamental matrix produce different errors in facing different motion scenes, but the homography combined with optical flow method can acquire dynamic regions in the image, while the fundamental matrix and polar line constraint can only filter feature points.