# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 3 Simulation Results and Performance Analysis
### Quantitative Analysis
The initialization cannot be completed until the next time the MAV leaves the platform, however, the map created does not include the map information of the first three stages. After the recovery tracking of the algorithm in this paper, the Pose recovery of 41.36% can be achieved on the MH02 sequence. Since some frames are stationary, namely, the pose difference is small, the actual trajectory recovery is 14.91%, about 9.771 m. It can be observed from Table 1 that the initialization effect of ORB-SLAM3 is poor in the factory environment; thus, the effect is better after the algorithm is improved in this paper. In the sequence of room environment in V101\(\sim\)V203, ORB-SLAM3 can be initialized quickly, so the effect after recovery and tracking has little improvement, and the recovered trajectory length is only about half a meter away. Secondly, for example, Table 2, this paper also counts the root mean square error (RMSE) and standard deviation of the ATE to reflect the improvement of robustness and stability of the proposed algorithm compared with the ORB-SLAM3. RMSE can well reflect the global consistency, such as Equations (15) and (16),  
\[RMSE(F_{1:n}):=(\frac{1}{n}\sum_{i=1}^{n}\|trans(F_{i})\|^{2})^{\frac{1}{2}}, \tag{15}\]  
\[F_{i}:=Q_{i}^{-1}SP_{i}, \tag{16}\]  
where \(\|trans(F_{i})\|\) represents the translation part of the ATE, \(Q_{i}\in SE(3)\) is the ground truth, \(P_{i}\in SE(3)\) is the estimated pose, and \(S\in SE(3)\) is the transformation matrix from \(P_{i}\) to \(Q_{i}\). The improvement of the time efficiency of the proposed algorithm is analyzed by calculating the time spent by the SLAM system in the loop closure detection and the mean tracking time of each frame. Table 3 reflects the improvement of each sequence in the Euroc dataset. After comparing and analyzing the proposed algorithm with ORB-SLAM3 using Euroc dataset, it can be observed that under the same conditions (compared with the complete trajectory of the ground truth), RMSE, standard deviation, mean tracking time and loop \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Standard Deviation**} & \multicolumn{2}{c}{**Mean Time**} & \multicolumn{2}{c}{**Detect Time**} \\ \cline{2-9}  & **ORBSLAM3** & **Ours** & **ORBSLAM3** & **Ours** & **ORBSLAM3** & **Ours** & **ORBSLAM3** & **Ours** \\ \hline MH01 & 0.042274 & **0.021442** & 0.028004 & **0.008902** & 0.03448 & **0.02702** & 2.00182 & **0.72322** \\ MH02 & 0.089979 & **0.025743** & 0.035764 & **0.013315** & 0.03591 & **0.02768** & 1.46197 & **0.66376** \\ MH03 & 0.144068 & **0.035220** & 0.095012 & **0.017778** & 0.03232 & **0.02814** & 2.31237 & **1.09805** \\ MH04 & 0.140039 & **0.134385** & 0.068027 & **0.060891** & 0.03118 & **0.02470** & 2.58796 & **1.55749** \\ MH05 & **0.492024** & 0.057585 & 0.300062 & **0.026758** & 0.03073 & **0.02590** & 2.56432 & **1.40208** \\ V101 & 0.058089 & **0.035530** & 0.027776 & **0.012011** & 0.03059 & **0.02986** & 2.24815 & **0.95563** \\ V102 & 0.074244 & **0.017952** & 0.063103 & **0.011832** & 0.02831 & **0.02615** & 2.18541 & **0.85070** \\ V103 & 0.019957 & **0.018903** & **0.008785** & 0.00886 & 0.02824 & **0.02493** & 2.36221 & **0.85074** \\ V201 & 0.043577 & **0.024846** & 0.027442 & **0.014310** & 0.02632 & **0.02508** & 3.27765 & **1.27054** \\ V202 & 0.024106 & **0.015883** & 0.009851 & **0.006280** & 0.02998 & **0.02612** & 2.89432 & **1.11357** \\ V203 & 0.033258 & **0.024923** & 0.017620 & **0.014805** & 0.02701 & **0.02465** & 2.54009 & **1.16674** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of absolute trajectory error (ATE) and time efficiency. The mean time (unit in second) is the time consumed by tracking each frame, and the detection time (unit in millisecond) is the time consumed by each frame in the loop closure detection.