# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 1 Introduction
At the same time, the advantages, disadvantages, and challenges of implementing Visual SLAM and VOE have not been presented. However, DL requires a large amount of learning data, especially to have a model capable of estimating environmental maps in many different environments and contexts, enriching learning data is an urgent requirement. Building a dataset to evaluate Visual SLAM and VOE algorithm models faces many challenges such as large environments with difficulties in collecting and synchronizing data, data collection equipment, annotation data, and labeling data, as well as external factors affecting the data collection process. Previous datasets such as the KITTI [9; 10; 11], TUM RGB-D SLAM [12], and ICL-NUIM [13] datasets have been collected and used to evaluate models for nearly a decade; these databases were collected from image sensors based on old technology, such as TUM collected and synchronized based on Microsoft Kinect. Therefore, collecting databases to evaluate Visual SLAM and VOE algorithmic models is necessary. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Year** & **Methods** & \
\begin{tabular}{c} **Type of** \\ **Datasets** \\ \end{tabular} &
\begin{tabular}{c} **Survey** \\ **of DL** \\ \end{tabular} \\ \hline
[14] & 2017 &
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} & RGB-D & No \\ \hline
[15] & 2019 & \
\begin{tabular}{c} Visual-inertial \\ SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} Stereo, \\ RGB-D \\ \end{tabular} & No \\ \hline
[16] & 2020 &
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[17] & 2020 &
\begin{tabular}{c} Visual SLAM \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[18] & 2020 & \
\begin{tabular}{c} Semantic SLAM, \\ Visual SLAM \\ \end{tabular} &
\begin{tabular}{c} Monocular, \\ RGB-D, \\ Stereo \\ \end{tabular} & Yes \\ \hline
[19] & 2021 &
\begin{tabular}{c} Visual SLAM \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[20] & 2022 &
\begin{tabular}{c} Embedded SLAM, \\ Visual-inertial \\ SLAM, \\ Visual-SLAM, \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[6] & 2022 & \
\begin{tabular}{c} Visual SLAM, \\ Visual-SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} Sonar, \\ Laser, \\ LiDAR, \\ RGB-D, \\ Monocular, \\ Stereo \\ \end{tabular} & Yes \\ \hline
[21] & 2022 &
\begin{tabular}{c} Visual SLAM \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[22] & 2022 &
\begin{tabular}{c} Visual SLAM \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[23] & 2022 &
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[24] & 2022 & \
\begin{tabular}{c} Semantic \\ Visual SLAM \\ \end{tabular} &
\begin{tabular}{c} LiDAR, \\ RGB-D, \\ Monocular, \\ Stereo \\ \end{tabular} & Yes \\ \hline
[25] & 2022 & \
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} RGB-D, \\ GPS \\ \end{tabular} & No \\ \hline
[26] & 2022 &
\begin{tabular}{c} Visual SLAM \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[27] & 2022 &
\begin{tabular}{c} LiDAR, \\ RGB-D, \\ Point cloud \\ \end{tabular} & Yes \\ \hline
[28] & 2023 &
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} & RGB-D & Yes \\ \hline
[29] & 2023 & \
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} Monocular, \\ RGB-D, \\ Stereo \\ \end{tabular} & Yes \\ \hline
[7] & 2023 & \
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} Monocular, \\ RGB-D, \\ Stereo \\ \end{tabular} & Yes \\ \hline
[30] & 2024 & \
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} Monocular, \\ RGB-D \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Surveys on the Visual SLAM and VOE from 2017 to 2024. Although there have been surveys on DL-based Visual SLAM and VOE, these surveys only cover one aspect such as surveying the method of using DL in Visual SLAM and VOE systems.