## 5 Conclusions  
In this article, the most representative open source monocular RGB SLAM and VO available implementations were tested following a taxonomy to determine the advantages and limitations of each method and classification, providing the reader a guide to correctly select the method that fits their needs or to select a path to make future contributions to the tested methods and classifications. After experimentation, it can be concluded that the monocular SLAM and VO methods need to be evaluated on larger datasets in a large variety of environments, motion patterns, and illumination conditions to be effectively compared with the state of the art, as demonstrated in this study for methods like the DSM, CNN-DSO, and DF-ORB-SLAM that did not match the expected results on the TUM-Mono dataset. \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Category** & **Advantages** & **Limitations** \\ \hline SVO [13] & Classic hybrid & Low computational cost. Good documentation and open-source availability for implementation in diverse configurations. & Frequent trajectory loss issues. Initialization issues. Critical execution errors due to the absence of a relocalization module. \\ \hline LDSO [30] & Classic sparse-direct & Low computational cost. Similar to DSO, detailed and precise 3D reconstruction. Ease of implementation. Lopo closure module. Slightly but not significantly better performance than the DSO in translation and rotation error. Best performance in the translation and RMSE metrics (compared to DSO), but without considerable difference. & Requirement of a specific complex camera calibration. Significantly worse performance than the DSO in the end-segment error metric. \\ \hline DSM [31] & Classic sparse-direct & Detailed and precise 3D reconstruction. Robust execution in most of the environments and motion patterns. Complete and interactive GUI. & Requirement of more computational capabilities than the rest of the sparse-direct methods. Significant underperformance compared to most of the sparse-direct methods. \\ \hline CNN-DSO [15] & ML sparse-direct & Detailed and precise 3D reconstruction. Robust to multiple environments and motion patterns. Best performance in scale error metric. & Presence of outliers in the 3D reconstruction. Significantly better performance in the rotation error metric by the DSO. Difficult to implement. Specific hardware requirement. \\ \hline DynaSLAM [32] & ML sparse-indirect & Multiple input modes. Ease of implementation. Robustness to multiple environments. Ability to detect, segment, and remove information of moving objects. Especially recommended for dynamic environments. Fewer trajectory loss issues than ORB-SLAM2. & Accumulation of drift while relocalizing. Sparse 3D reconstruction. Increase in complexity over the ORB-SLAM2. Specific hardware requirement. \\ \hline CNN-SVO [11] & ML hybrid & Considerable reduction in the trajectory loss issues compared to the SVO. Initialization issues. Reduction in the number of execution issues compared to the SVO. Improved performance over the ORB-SLAM2 in the rotation, translation, scale, and RMSE metrics. Significant improvement over its classic version in all the metrics. & Considerable presence of outliers in the 3D reconstruction. Imprecise and sparser 3D reconstruction. Complex implementation. Specific hardware requirement. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._The sparse-direct category of the taxonomy achieved the significantly best results among all the ten methods in the translation, rotation, scale, and RMSE metrics outputting the most detailed and precise 3D reconstructions of the tested methods. At second best, the sparse-indirect category achieved good ego-motion estimation but output sparser 3D reconstructions that might not be suitable for many applications, presenting trajectory loss issues and evidencing worse performance indoors.