## 4 Discussion  
In the previous studies of [39, 40], the authors compared the DSO, LDSO, ORB-SLAM2, and DynaSLAM algorithms on the same TUM-Mono benchmark, and their findings mostly matched what we observed during this evaluation. However, we extended their study considerably by implementing six additional methods following the taxonomy described in [9] and performed an appropriate statistical analysis to determine the significant differences in each system's performance. Thus, we could observe the classification advantages and limitations for classic geometric-based approaches. The classic approaches can be classified as sparse-indirect, dense-indirect, dense-direct, sparse-direct, and hybrid. For the sparse-indirect, we selected its gold standard, ORB-SLAM2, and we can report that, in our experience, it is an excellent method that demands low computational power and has an average performance not being the best but still working well outdoors. Its main limitations are its poor indoor performance and large drift and scale error accumulation during relocalization. We believe the ORB-SLAM2 has achieved high popularity due to its low computational power consumption and ease of implementation. For the dense-indirect category, we selected the DF-ORB-SLAM, an ORB-SLAM2 open-source implementation with an additional optical flow estimation module that allows the ORB-SLAM2 to work with more image information. In this case, we observed that the optical flow module increased the computational cost of the algorithm and slightly reduced the occurrence of trajectory loss issues. However, adding image information for feature extraction also introduced noise in the estimation steps, significantly increasing the translation, rotation, scale error metrics, and RMSE. For dense-direct approaches, we selected their gold standard, the LSD-SLAM, one of the first proposed direct methods. In this case, we found that the performance was significantly the poorest of all the evaluated methods. This situation could be due, in particular, to the frequent initialization errors and trajectory loss in most of the benchmark sequences matching what [PERSON] et al. reported in their study [21]. For the sparse-direct classification, we selected the most iconic VO system, the DSO. This system significantly outperformed the methods of the rest of the classic classifications by a large margin, demonstrating an impressive performance indoors and outdoors. The DSO was slightly outperformed by its neuronal version on the scale metric and slightly outperformed by the LSDO in the translation and RMSE metric, but the differences were not significant. This behavior lets us conclude that even its LDSO, CNN-DSO, and DSM extensions do not significantly outperform the DSO, and this method can still be considered a great avenue for future work, improvements, and contributions. Additionally, it must be mentioned that, in contrast to what was reported in [31], the DSM method did not outperform its predecessor, the DSO, in the TUM-Mono dataset, even after implementing a complete SLAM pipeline to extend the DSO. We believe the DSM is a robust system that can contribute to the sparse-direct category. However, the authors used the extended pinhole radial-tangential camera model instead of the complete photometrical camera calibration provided in the original DSO, including the intrinsic, photometric, and nonparametric vignette calibration. As reported in [40], this situation considerably contributed to the correct execution of the algorithm and allows it to obtain the best results. Then, for the hybrid approaches, we tested the SVO, which combines direct and indirect formulation paradigms in its pipeline. The SVO was second to last in our comparison, being significantly outperformed by most of the methods in this study, only performing significantly better than the LSD-SLAM, in line with what was reported in [21]. However, with this analysis, we could observe that it at least performed better than the dense-direct method.