# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 3 Simulation Results and Performance Analysis
### Qualitative Analysis
However, the proposed algorithm completes the loop correction process only after two such calculations. It is worth noting that on the same sequence, the loop completion time of the proposed algorithm is slightly earlier than that of ORB-SLAM3. This is because when ORB-SLAM3 calculates and optimizes the pose, it takes too much time, resulting in missing the most  
Figure 6: Comparison of ATE (raw date). (**a**) and (**b**) are the ATE comparison between the proposed algorithm and ORB-SLAM3 in MH02 sequence and V203 sequence respectively. The yellow line represents the result of the proposed algorithm, and the blue line represents the result of ORB-SLAM3. Figure 7: Comparison of ATE (box plot). (**a,b**) are the comparison of ATE between the proposed algorithm and ORB-SLAM3 in MH02 sequence and V203 sequence, respectively, by box plot. The results of ORB-SLAM3 are shown in blue and ours are shown in brown. suitable loop candidate frame, and only finding the appropriate loop candidate frame after the calculation is completed. Around 230 of the sequence, the loop closure detection process is mainly concentrated here. For example, loop closures can be detected by the bag-of-words model in both Figure 9a,b, but only the keyframe pair in Figure 9a can finally pass the loop closure check. In Figure 9b, the computer reappears at the same angle, so some descriptors are successfully matched. By matching using the bag-of-words model, we think that the camera came to the same location. However, in this keyframe pair, the computer is at different distances in the image, and the roll angle of the camera changes significantly. In the loop closure check, because the scale of the feature points has a large difference or the angle of the rpy direction changes too much, we think that the loop closure at this time has a great risk, and thus reject the loop closure. However, this situation can be effectively avoided by improved methods. For example, the grayscale histogram similarity of the keyframe pair in Figure 9a is 0.9782, and in Figure 9b is 0.6659. Therefore, the loop closure in Figure 9b will be eliminated early. More importantly, when we combine Figures 6b, 8 and 9, we can understand why there are some larger ATEs at certain time spots. We found that in the V203 sequence, its  
Figure 8: Computation time used to loop closure detection for each frame in V203. Figure 9: Pairs of keyframes detected by loop closures in V203 sequences and their grayscale histograms. **(a)** Good loopback situation. The loopback detection thread can usually complete the loopback here. **(b)** Bad loopback situation. The loopback detection thread is prone to loopback rejections here. ATE was larger during the period when loop closures were easily identified. This is because different threads need to coordinate the allocation of computer resources when the SLAM system processes tasks. Moreover, when ORB-SLAM3's loop closing thread requests more time to process the loopback, the work of tracking the thread becomes more challenging. As a result, the accuracy of the Poses we obtain will decrease. Of course, not all larger ATEs are only affected by time efficiency. As shown in Figures 9b and 10, motion blur and large scale changes will affect our accuracy. In general, both motion blur and scale differences will affect the quality of the feature points extracted by the SLAM system. When we use these feature points to track and estimate pose, there will be fluctuations in accuracy.