## 3 Results
### Comparative Analysis
Figure 9 and Table 4 show many significant differences between the ten compared methods on the alignment error and RMSE metrics. Regarding the start-segment alignment error, the DSO, DynaSLAM, and ORB-SLAM methods outperformed the rest of the algorithms. Despite the DSO slightly reducing the average start-segment alignment error by around 7.28% and 7.81% compared to the DynaSLAM and ORB-SLAM2, the differences were not significant. The rest of the sparse-direct methods, LDSO, CNN-DSO, and DSM, performed significantly worse than the DSO by an average of 49.84%, 55.77%, and 74.83%, respectively, in the start-segment alignment error metric. For the feature-based methods, the DynaSLAM and DSO performed significantly better than the DF-ORB-SLAM, while the DF-ORB-SLAM achieved an error significantly lower than the CNN-SVO, SVO, and LSD-SLAM. When comparing the CNN-SVO with its predecessor SVO, the difference was significant, where the neural version reduced the start-segment alignment error by an average of close to 37.86%. The LSD-SLAM achieved the worst start-segment alignment error of the ten methods, significantly. By observing the end-segment alignment error, it was found that the sparse-direct methods significantly outperformed the rest of the compared methods. The DSO significantly outperformed all the evaluated methods, including the rest of the sparse-direct methods, the LDSO, CNN-DSO, and DSM, reducing the average alignment error by around 47.85%, 32.50%, and 73.06%, respectively. In the sparse-indirect category, the DynaSLAM and ORB-SLAM2 performed significantly better than the DF-ORB-SLAM, but even though the ORB-SLAM2 reduced the average end-segment error by approximately 7.49%, the difference was not significant. The CNN-SVO end-segment alignment error was significantly lower than the error of the SVO, reducing this metric by approximately 47.31%. The LDSO performed significantly worse than the rest of the methods. For the RMSE metric, the sparse-direct methods performed significantly better than the rest, where the LDSO achieved RMSE values around 0.32% and 6.68% lower than the DSO and CNN-DSO; the differences were not significant. The LDSO performed significantly better than the DSM, with an average RMSE around 10% lower. In the feature-based classification, the DynaSLAM performed significantly better than the ORB-SLAM2 and the DF-ORB-SLAM, reducing the RMSE metric by approximately 24.49% and 34.41%, respectively. For the hybrid methods, the CNN-SVO performed significantly better than the SVO, reducing the RMSE by around 34.83%. As with the rest of the metrics, the LSD-SLAM performed significantly worse than the other methods in terms of the RMSE metric. Finally, in Figure 10, we present the sample trajectories obtained by the three overall best methods evaluated in this comparison study. To exemplify the behavior of the algorithms in different environments, we selected the sequence _seq-02_ of the TUM-Mono  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \ \begin{tabular}{c} **Start-Segment** \\ **Alignment Error** \\ \end{tabular}  & \begin{tabular}{c} **End-Segment** \\ **Alignment Error** \\ \end{tabular}  & **RMSE** \\ \hline Kruskal–[PERSON] & \(\chi^{2}=4575.7\) & \(\chi^{2}=3718\) & \(\chi^{2}=530.78\) \\ general test & \(p_{value}=2.2e\times 10^{-16}\) & \(p_{value}=2.2\times 10^{-16}\) & \(p_{value}=2.2\times 10^{-16}\) \\ \hline DSO & 0.003974759 [FOOTNOTE:9]Footnote 9:  
dataset as an example for indoors and the sequence _seq-29_ as an example for outdoors. In addition, we provide video samples of the execution of each algorithm as supplementary material in the GitHub repository: \"[[https://github.com/erickherreraresearch/Monocula](https://github.com/erickherreraresearch/Monocula)]([https://github.com/erickherreraresearch/Monocula](https://github.com/erickherreraresearch/Monocula)) rPureVisualSLAMComparison accessed on 16 June 2023\", along with all the.txt result files of each algorithm run for reproducibility.