# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 3 Simulation Results and Performance Analysis
### Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **MH01** & **MH02** & **MH03** & **MH04** & **MH05** & **V101** & **V102** & **V103** & **V201** & **V202** & **V203** \\ \hline
**RMSE** & 49.28\% & 85.21\% & 75.55\% & 4.04\% & 88.31\% & 38.84\% & 75.82\% & 5.28\% & 41.12\% & 34.11\% & 25.06\% \\
**SD** & 68.21\% & 85.93\% & 81.29\% & 10.49\% & 91.08\% & 56.76\% & 81.25\% & \(-\)1.15\% & 47.85\% & 36.25\% & 15.98\% \\
**Mean Time** & 21.64\% & 22.92\% & 12.93\% & 20.78\% & 15.72\% & 2.39\% & 7.63\% & 11.72\% & 4.71\% & 12.88\% & 8.74\% \\
**Detect Time** & 63.87\% & 54.61\% & 52.51\% & 39.82\% & 45.32\% & 57.49\% & 61.07\% & 63.99\% & 61.24\% & 61.54\% & 54.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Improvement of RMSE, SD, mean tracking time and loop closure detection time. closure detection time of the proposed algorithm are reduced. The RMSE of 11 sequences decreased by 47.51% on average, and the accuracy was improved significantly. Among them, the RMSE on MH01\(\sim\)MH05 sequence is much lower, which is because the number of Pose recovered by the algorithm in this paper on these sequences is up to several hundred, and the final reconstructed map trajectory is long. More information can be provided to the map after trajectory recovery, and keyframes created during tracking can also be involved in global optimization to further improve accuracy. At the same time, because the time efficiency of the SLAM system is improved, there are more sufficient computer resources to complete BA optimization and more stable tracking image frames. The RMSE reduction in V101\(\sim\)V203 is less than in the factory environment. This is because in the sequence of these room scenes, the image frame and IMU data obtained by SLAM system are good and can complete rapid initialization, so the accuracy improvement effect of this algorithm on these sequences is limited. Secondly, the average decline of standard deviation on Euroc dataset is 52.18%, which indicates that the proposed algorithm is more stable than the original algorithm and can run SLAM system consistently. In addition, this algorithm can also significantly improve the time efficiency. In the Euroc dataset, tracking the time efficiency increased by 12.91% on average, and the detection time efficiency increased by 55.96% on average. Similarly, the time efficiency is significantly improved in the sequences with difficulty in initialization, which is due to the computational lightweight of the map restoration algorithm in this paper. Of course, the improvement is not high in the easy initialization sequence. It is worth noting that V103, V202 and V203 sequences are easy to initialize, but there are loop closures in their sequences. After the loop closure, the number of common-view keyframes will increase significantly, so that more frames can be eliminated, and the detection time and tracking time can be significantly reduced. Therefore, compared with ORB-SLAM3, the proposed algorithm can first generate a complete map, and then significantly improve the accuracy and time efficiency of the difficult initialization sequence. According to the characteristics of the algorithm, in the environment that initialization is easier to fail and the number of loop closure is more, or when the system runs longer, the accuracy and time efficiency of the system are improved more obviously.