# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 3 Visual SLAM and Visual Odometry Using Deep Learning: Survey
### Deep Learning-Based Module for Visual SLAM and Visual Odometry
#### 3.1.4 DL Modules Add to the Visual SLAM Algorithm
error (degree) and translation (Trans.) error (m). Some research results of the camera relocalization module based on DL on the 7-Scenes [146]. Another DL-based module is distance estimation. The results of studies performing distance estimation based on image data obtained from the environment are shown in Table 10. The results were evaluated on the KITIT dataset with the following measurements: the \(RMSE\) has been presented above; the \(AccDev\) is the accuracy with one-meter deviation; the \(Acc\) is more accurate than on the accurate one. The \(RMSE\) result should be as small as possible, while larger \(Acc\) and \(DevAcc\) values are better. Another DL-based module is scene reconstruction. The results of studies performing 3D reconstruction scenes based on image data (RGB-D) obtained from the environment are shown in Tables 10 and 11. In Table 10, the quality results of 3D reconstruction were evaluated based on the KITTI 2012 dataset and ScanNet++ dataset [171] with measures of accuracy (\(Acc\)), \(RMSE\), and \(AccDev\). With the measure \(RMSE\), the smaller the value the better; with the measure \(AccDev\), the closer the value is to 1, the better. The results of DSO-stereo are very high, where the \(AccDev\) and \(Acc\) are equal to 1.0. The results show that RTG-SLAM has the best result, with an \(Acc\) measure of 0.0095, and Point-SLAM has the best result, with an \(AccDev\) measure of 0.9912. The Point-SLAM [120], SplaTAM [113], and RTG-SLAM [112] methods have very good results on the ScanNet++ dataset. In Table 11, the 3D reconstruction scene methods are based on RGB or depth images of the NYU RGB-D V2 [49], KITTI 2012, and Make3D [50] datasets. When using RGB images as input, methods often use the method of estimating the depth of the image and then combining it with color images to build a 3D scene with point cloud data. These studies often perform and improve image depth estimation models. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Authors**} & \multicolumn{2}{c}{**Dataset/ Measure/ Measure/ Methods**} & \multicolumn{2}{c}{**KITTI 2012 Dataset (03, 04,**} & \multicolumn{2}{c}{**KITTI 2012 Dataset (09,10)**} & \multicolumn{2}{c}{**ScanNet++**} \\  & \multicolumn{2}{c}{**Measu/ Methods**} & \multicolumn{2}{c}{**05, 06, 07,100**} & & & & & [171] & Dataset \\ \cline{3-11}  & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{_Acc_} & \multicolumn{2}{c}{_AccDev_} & \multicolumn{2}{c}{_RMSE_} & \multicolumn{2}{c}{_Acc_} & \multicolumn{2}{c}{_AccDev_} & \multicolumn{2}{c}{_Acc_} & \multicolumn{2}{c}{_AccDev_} \\ \hline
[176]/2015 & ORB-SLAM-mono & 7.4623 & 0.0221 & 0.0368 & - & - & - & - & - \\ \hline
[177]/2016 & DSO-mono & 7.3854 & 0.0241 & 0.0452 & - & - & - & - & - \\ \hline
[178]/2017 & PMO & 0.7463 & 0.7183 & 0.9633 & - & - & - & - & - \\ \hline
[179]/2017 & DSO-stereo & 0.0756 & 0.9387 & 1 & - & - & - & - & \\ \hline
[69]/2018 & GeoNet & - & - & - & 6.2302 & 0.0306 & 0.0544 & - & - \\ \hline
[180]/2019 & SRNN & 0.6754 & 0.6121 & 0.9667 & - & - & - & - & - \\ \hline
[180]/2019 & SRNN-se & 0.6526 & 0.5801 & 0.9727 & - & - & - & - & - \\ \hline
[180]/2019 & SRNN-point & 0.5234 & 0.6267 & 0.9822 & - & - & - & - & - \\ \hline
[180]/2019 & SRNN-channel & 0.5033 & 0.6487 & 0.9873 & - & - & - & - & - \\ \hline
[181]/2019 & DistanceNet-FlowNetS & 0.5544 & 0.6292 & 0.9752 & - & - & - & - & - \\ \hline
[181]/2019 & DistanceNet-Reg & 0.5315 & 0.6648 & 0.9855 & - & - & - & - & - \\ \hline
[181]/2019 & DistanceNet-LSTM & 0.4167 & 0.6871 & 0.9896 & - & - & - & - & - \\ \hline
[181]/2019 & DistanceNet-BCE & 0.3925 & 0.7158 & 0.993 & - & - & - & - & - \\ \hline
[181]/2019 & DistanceNet & 0.3901 & 0.6984 & 0.9916 & 0.4624 & 0.6669 & 0.9841 & - & - \\ \hline
[48]/2019 & SfMLearner & - & - & - & 7.5671 & 0.0216 & 0.0505 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of Visual SLAM when using the DL distance estimation module.