## 3 Results
### Comparative Analysis
Finally, it must be mentioned that the large error observed in the LSD-SLAM, SVO, and CNN-SVO methods can be attributed to the severe initialization and relocalization problems that the algorithms presented during the evaluations in the TUM-Mono dataset. As mentioned, the alignment error considers the translation, rotation, and scale errors equally. Therefore, it is equivalent to the translational RMSE when aligned to the start and end segments (the first and last 10-20 s of each sequence), for which the ground truth is available. The cumulated alignment error for each algorithm is presented in Figure 5.  
Figure 4: Translation \(e_{t}\), rotation \(e_{r}\), and scale \(e_{s}^{\prime}\) accumulated errors for the ten evaluated algorithms. Figure 5: Start and end segment alignment error, corresponding to the RMSE of the alignment error when compared with the start and end segment’s ground truth. Figure 5 shows that the ORB-SLAM and DynaSLAM performed slightly better than the sparse direct methods for start-segment alignment errors. However, on the end segment, the cumulative drift effect was lower on the sparse-direct methods ratifying the results observed in Figure 4. In addition, it can be noticed that the CNN-DSO performed better than the DSO, suggesting that integrating the Single Image Depth Estimation (SIDE) CNN improved the DSO bootstrapping by adding the prior depth information, whereas the end-segment performance of both algorithms was similar. Moreover, the addition of the Mask R-CNN in DynaSLAM was used to remove scenes' moving objects information and did not represent a clear improvement in algorithm performance in the start segment, but, as shown in Figure 5, the benefits of adding the CNN can be observed over the end segment by the reduction in the accumulated drift. Additionally, for the hybrid approaches, the addition of the MonoDepth CNN made a paramount contribution in helping to overcome SVO loss of trajectory issues. Similar to the results in Figure 1, the overall alignment error results suggest that the sparse-direct methods performed better, followed by the sparse-indirect, dense-indirect, hybrid, and finally the dense-direct, reaching a threshold error in around 50 runs. As suggested by [6], we examined the dataset motion bias for each algorithm by running each method ten times forwards and ten times backward in such modalities and combining both to visualize how much each algorithm is affected. This situation allowed us to consider the importance of evaluating the SLAM and VO methods in large datasets, covering as many environments and motion patterns as possible. The dataset motion bias for each method is presented in Figure 6.  
In Figure 6, it can be noticed that the DSO, LDSO, and SVO were not seriously affected by motion bias. In contrast, different motion patterns considerably affected the ORB-SLAM2, DynaSLAM, and DF-ORB-SLAM. This can be observed in the performance differences when running forwards versus backward. This behavior provides a reference for the consistency and robustness of each algorithm for using them in different environments or applications. It can be observed that the CNN-DSO on forward-only modality outperformed its classic version, but it suffered from a larger motion bias effect affecting its overall performance; while DynaSLAM and CNN-SVO outperformed their classic versions and presented less motion bias effect, representing an additional robustness improvement over their classic versions. Figure 6: The dataset motion bias for each method was evaluated by running all sequences forwards and backward, as well as their combination (bold). Figure 7 shows the color-coded alignment error for each of the 50 TUM-Mono sequences for each run forward and backward to observe which specific sequences were challenging for each algorithm.