# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 4 Challenges and Discussion
### Performances of Visual SLAM and VOE Systems  
DL has delivered convincing results in building Visual SLAM and VOE. However, DL is a method based on statistical machine learning, so the results in the steps of the Visual SLAM and VOE system-building process all have certain errors. Based on the model illustrated in Figure 1, errors can accumulate, and the output can have very large errors concerning the original data. To minimize these errors, end-to-end models were built based on DL. However, the accuracy was only partially improved; the results are presented in Table 12.  
Most of the studies using DL to build Visual SLAM and VOE often exploit environment features based on color images and depth images obtained from the environment. The features extracted using DL are mainly space. When moving in the environment, the data obtained from the environment is usually a sequence of frames. Therefore, temporal features need to be researched and extracted to improve the performance of environmental map construction. Another issue concerns the performance of DL learning methods, with 3D real-world spaces containing many environmental challenges such as environmental complexity, moving objects, lighting, etc. These factors all affect the performance of the learning model. Therefore, with the DL method of learning the environment, methods often have to use supervised learning methods to learn features extracted from the environment, such as in the studies [169; 200; 201; 202; 203; 204; 205; 206; 207; 208; 209; 210; 211; 212]. The method of using unsupervised data is often performed in very specific and uniform environmental conditions, such as in the studies [202; 204; 205; 213; 214]. To evaluate the performance of models to build environmental maps and VOE, it is necessary to perform on standard datasets of Visual SLAM and VOE. However, the above studies often evaluated famous datasets such as KITTI, NYUDepth [49], Make3D [50], Cityscapes [51], TUM RGB-D SLAM, ICL-NUIM, MPI Sintel, Middlebury, Flying Chairs [55], Foggy [83], Webcam [90], Oxford [98], EF [99], and HPatches [100]. The datasets were \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{3}{*}{**Authors/Years**} & \multirow{3}{*}{**Methods**} & \multicolumn{3}{c}{**KITTI**} & \multicolumn{1}{p{56.9 pt}}{**KITTI**} & \multicolumn{1}{p{56.9 pt}}{**KITTI**} \\  & & \multicolumn{1}{p{56.9 pt}}{**Dataset/**} & \multicolumn{1}{p{56.9 pt}}{**Dataset**} & \multicolumn{1}{p{56.9 pt}}{**2012**} & \multicolumn{1}{p{56.9 pt}}{**Dataset**} \\  & & \multicolumn{1}{p{56.9 pt}}{**Measual/**} & \multicolumn{1}{p{56.9 pt}}{**(00, 02, 05, 07, 08)**} & \multicolumn{1}{p{56.9 pt}}{**(09, 10)**} & \multicolumn{1}{p{56.9 pt}}{**(Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05, Seq.05 & \(t_{\text{ref}}\) (\%) & \(\tau_{\text{ref}}\) (Degrees) & \(t_{\text{ref}}\) (\%) & \(\tau_{\text{ref}}\) (Degrees) \\ \hline
[200]/2022 & Self/VO & Trajectory estimation & 0.9 & 0.44 & 1.88 & 1.23 & - & - \\ \hline
[200]/2022 & Self/VO (no IMU) & Trajectory estimation & - & - & 2.41 & 1.62 & - & - \\ \hline
[200]/2022 & Self/VO (LSTM) & Trajectory estimation & - & - & 2.07 & 1.32 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 12: _Cont._proposed between 2005 and 2020, making them very old and no longer suitable for the development of current image sensor technology. Although there have been many studies to build VOE and Visual SLAM systems, there are also many convincing results based on DL approaches. Especially, DL-based methods in the end-to-end direction or for each module use DL networks to perform tasks. Monitoring, evaluating, and determining the effectiveness of features extracted from DL networks are very difficult to apply. DL methods with different depths, different numbers of layers, and complex architectures make improvement very difficult.