# 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
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & \multicolumn{1}{c}{\begin{tabular}{c} **SceneNet** \\ **RGB-D** \\ **[145]** \\ \end{tabular} } & \
\begin{tabular}{c} **KITTI** \\ **2012** \\ **Dataset** \\ \end{tabular} &
\begin{tabular}{c} **7-Scenes** \\ **Dataset** \\ \end{tabular} \\ \cline{3-5}
**Authors/Users** & \begin{tabular}{c} **Datasets/** \\ **Measu./** \\ **Methods** \\ \end{tabular} & \begin{tabular}{c} **RMSE of** \\ **ATE** \\ **(cm)** \\ \end{tabular} & \
\begin{tabular}{c} **RMSE of** \\ **ATE** \\ **(cm)** \\ \end{tabular} &
\begin{tabular}{c} **Dense** \\ **Correspondence** \\ **Reprojection** \\ **Error** \\ **(DCRE) **(cm)** \\ \end{tabular} \\ \hline \([150]\)/\(2015\) & InfiniTAM(IM),S1 & 22.486 & - & - \\ \hline \([150]\)/\(2015\) & InfiniTAM(IM),S2 & 28.08 & - & - \\ \hline \([150]\)/\(2015\) & InfiniTAM(IM),S3 & 13.824 & - & - \\ \hline \([150]\)/\(2015\) & InfiniTAM(IM),S4 & 34.846 & - & - \\ \hline \([151]\)/\(2017\) & BundleFusion (BF),S3 & 4.164 & - & - \\ \hline \([151]\)/\(2017\) & BundleFusion (BF),S1 & 5.2 & - & - \\ \hline \([151]\)/\(2017\) & BundleFusion (BF),S2 & 5.598 & - & - \\ \hline \([151]\)/\(2017\) & BundleFusion (BF),S4 & 7.742 & - & - \\ \hline \([152]\)/\(2017\) & PoseNet17 & - & - & 24 \\ \hline \([147]\)/\(2018\) & Maskfusion(MF),S4 & 18.972 & - & - \\ \hline \([147]\)/\(2018\) & Maskfusion (MF),S1 & 20.856 & - & - \\ \hline \([147]\)/\(2018\) & Maskfusion (MF),S2 & 22.71 & - & - \\ \hline \([153]\)/\(2018\) & PoseNet + log q & - & - & 22 \\ \hline \([147]\)/\(2018\) & Maskfusion (MF),S3 & 14.824 & - & - \\ \hline \([153]\)/\(2018\) & MapNet & - & - & 21 \\ \hline \([68]\)/\(2018\) & Vid2 Depth & - & - & 1.25 & - \\ \hline \([142]\)/\(2019\) & MID-fusion (MID),S1 & 5.98 & - & - \\ \hline \([142]\)/\(2019\) & MID-fusion (MID),S2 & 4.132 & - & - \\ \hline \([142]\)/\(2019\) & MID-fusion (MID),S3 & 5.1675 & - & - \\ \hline \([142]\)/\(2019\) & MID-fusion (MID),S4 & 5.3825 & - & - \\ \hline \([71]\)/\(2019\) & CC & - & 1.2 & - \\ \hline \([47]\)/\(2019\) & Struct2 Depth & - & 1.1 & - \\ \hline \([72]\)/\(2019\) & Monodepth2 & - & 1.6 & - \\ \hline \([74]\)/\(2020\) & EPC++ & - & 1.2 & - \\ \hline \([143]\)/\(2021\) & NeuralR-Pose & - & - & 21 \\ \hline \([75]\)/\(2021\) & Insta-DM & - & 1.05 & - \\ \hline \([141]\)/\(2022\) & ObjectFusion,S3 & 0.79 & - & - \\ \hline \([141]\)/\(2022\) & ObjectFusion,S1 & 0.964 & - & - \\ \hline \([144]\)/\(2022\) & ORGPoseNet & - & - & 21 \\ \hline \([141]\)/\(2022\) & ObjectFusion,S4 & 1.132 & - & - \\ \hline \([144]\)/\(2022\) & ORGMapNet & - & - & 20 \\ \hline \([53]\)/\(2022\) & Cowan & - & 1.15 & - \\ \hline \([53]\)/\(2022\) & Cowan-GGR & - & 1.05 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of Visual SLAM when using the DL pose estimation module. with full geometry information of the environment and information on 3D objects in the environment. In the phase of detecting and segmenting objects in the environment, the approach has been used and evaluated on many typical models such as Fast R-CNN, Faster R-CNN, YOLO, or the Single-Shot Multi-Box Detector (SSD). [PERSON] et al. [157] proposed a real-time semantic mapping system that includes two main tasks: The first is 3D geometric reconstruction using SLAM models, and the second is 3D object semantic segmentation using a CNN model to convert pixel label distributions of 2D images to 3D grids and propose a Conditional Random Field (CRF) model with higher-order cliques to enforce semantic consistency among grids. [PERSON] et al. [158] proposed online volumetric instance aware semantic mapping from RGB-D data based on geometric segmentation with object-like convex 3D segmentation of the depth image using a geometry-based method. Semantic instance-aware segmentation refinement is performed on the RGB image using Mask R-CNN; data association is performed on RGB-D image pairs; and map integration is performed using Voxblox TSDF-based dense mapping framework. [PERSON] et al.