# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 4 Challenges and Discussion
### Actual Implementation  
It can be seen that the biggest drawback of machine learning models as well as DL is the problem of changing the environment between training and experimentation. Although we have tried to learn all the situations that can occur in reality and tried to build an experimental environment close to the real environment, this is never enough, especially since many unusual situations arise in reality, and changing lighting conditions and timing causes the environment to change. The DL models may not learn these changes or may have not learned much. The results of building the environmental map of the Visual SLAM and VOE systems are not good, making the actual implementation difficult and yielding poor \begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{2}{c}{**7-Scenes**} \\  & **Dataset/** & & **Dataset** \\
**Authors/Years** & **Measu/** & **Measu/** & **Ang.** & **Trans.** \\  & & **Error** & **Error** \\  & & **(Degrees)** & **(m)** \\ \hline
[222,223]/2016 & PoseNet & 10.4 & 0.44 \\ \hline
[222,223]/2016 & Bayesian PoseNet & 9.81 & 0.47 \\ \hline
[222,223]/2016 & PoseNet-EulerF & 9.83 & 0.38 \\ \hline
[222,223]/2016 & PoseNet-EulerF-Aug & 8.58 & 0.34 \\ \hline
[222,223]/2016 & BranchNet-EulerF & 9.82 & 0.3 \\ \hline
[222,223]/2016 & BranchNet-EulerF-Aug & 8.3 & 0.29 \\ \hline
[152]/2017 & Geometric PoseNet & 8.1 & 0.23 \\ \hline
[224]/2017 & Hourglass & 9.5 & 0.23 \\ \hline
[225]/2017 & LSTM-Pose & 9.9 & 0.31 \\ \hline
[226]/2017 & BranchNet & 8.3 & 0.29 \\ \hline
[227]/2019 & MLFBPPose & 9.8 & 0.2 \\ \hline
[228]/2019 & ANNet & 7.9 & 0.21 \\ \hline
[229]/2019 & GPoseNet & 10.0 & 0.31 \\ \hline
[230]/2019 & AnchorPoint & 7.5 & 0.13 \\ \hline
[231]/2020 & AttLoc & 7.6 & 0.2 \\ \hline
[232]/2021 & GNN-RPS & 5.2 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of Visual SLAM when using the DL camera relocalization module. results. Nevertheless, the studies employing Visual SLAM and VOE have been evaluated on multiple databases such as KITTI 2012, KITTI 2015, NYU RGB-D V2, 7-Scenes, TUM RGB-D SLAM, GPW, ICL-NUIM, Mask-RCNN MC [149], SceneNet RGB-D [145], CARLA, Object Fusion [127], ADVIO [38], Euroc, Sintel Final, Middlebury, Flying Chairs, Foggy, etc, and in many different lighting conditions, indoors or outdoors, and performed at many different times. The results have been shown and compared above. However, the issue of practical implementation of Visual SLAM and VOE systems still faces many challenges and needs further research. Currently, to deploy Visual SLAM and VOE applications into practice, we use RGB-D image information obtained from the environment. The system needs to have calculation time equal to or close to reality (about 24 fps). To build a system with such computing time, the system needs to perform calculations on a relatively large GPU. Therefore, integrating GPUs onto embedded computers and mobile devices is also a challenge that needs further research. Another problem that has not been researched in the current research is the problem of understanding the scene and building a full environmental map (including the ability to estimate the path and reconstruct the 3D scene). In particular, 3D environment maps based on point cloud data have the same size and environment as real data. Figure 4 represents the estimated data-dense environment (point cloud data) based on ORB-SLAM2 in the TUM RGB-D dataset, though the studies that provide these results are very limited. These issues are very important in building applications that help the blind, self-propelled vehicles, and robots move in the environment. Although there are some concurrent studies between 3D reconstruction and Visual SLAM, the results are still very modest. As in [112]'s research, RTG-SLAM was proposed to allow the building of a real-time 3D reconstruction system from data obtained from RGB-D sensors. The features used are the Gaussian representation and Gaussian optimization method. The results were evaluated on the TUM RGB-D dataset, and only the calculation time was evaluated.