# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 1 Introduction
And there has not been a comprehensive study on the approach, advantages, disadvantages, challenges, evaluation dataset, evaluation metrics, and results. Based on the model of Visual SLAM and the VOE system, we first propose a taxonomy of Visual SLAM and the VOE framework to conduct a complete survey based on three methods to construct Visual SLAM and VOE from RGB-D images (1) using DL for the modules of the Visual SLAM and VOE systems; (2) using DL to supplement the modules of Visual SLAM and VOE systems; and (3) using end-to-end DL to build Visual SLAM and VOE systems, as shown in Figure 2. To have a detailed overview of the methods and results of implementing Visual SLAM and VOE, we have conducted a comprehensive and detailed survey of the methods; evaluation datasets; evaluation measures; results, advantages, and disadvantages; and the applications and challenge of DL-based Visual SLAM and VOE implementation methods with input data from RGB-D image sensors. In addition, we also aim at the applications of the surveyed studies. Currently, the price of the RGB-D sensor is much more reasonable than the LiDAR sensor. It is widely used, and the data obtained from the RGB-D image sensor provide data that are intuitive and close to the real environment surrounding the object. In particular in this study, we conduct a survey and analyze research in both the application direction of Visual SLAM and VOE. Nowadays, there has been strong development of computer hardware and image sensors. The Intel Real Scene D435 was launched in 2018, with a reasonable price and highly accurate data. At the same time, the RGB-D databases used to evaluate Visual SLAM and VOE models were proposed between 2005 and 2020 and are no longer suitable for \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
[31] & 2024 & \
\begin{tabular}{c} Visual SLAM, \\ VOE \\ \end{tabular} &
\begin{tabular}{c} Monocular, \\ RGB, Stereo, \\ LiDAR \\ \end{tabular} & No \\ \hline
[32] & 2024 &
\begin{tabular}{c} Visual SLAM, \\ Visual-Inertial SLAM \\ \end{tabular} & RGB-D, IMU & No \\ \hline
[33] & 2024 &
\begin{tabular}{c} Visual SLAM, \\ Visual-Inertial SLAM \\ \end{tabular} & RGB-D & Yes \\ \hline
[34] & 2024 & VOE &
\begin{tabular}{c} Monocular, \\ RGB \\ \end{tabular} & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)  
Figure 1: The general framework for building the Visual SLAM [6]. VOE systems typically include feature extraction, feature matching, loop closure, optimization, and pose estimation steps. Both VOE and Visual SLAM systems perform the front-end process on data collected from the environment, and the back-end development involves a regression process based on the extracted features in the front-end process. image sensor technology. In this paper, we used Intel Real Scene D435 to collect, prepare annotation data, and publish the TQU-SLAM benchmark dataset for evaluating the VOE model. At the same time, a comparative study for VOE on the TQU-SLAM benchmark dataset was also performed with an MLF-VO framework [35]. With the above works and the remaining points in previous studies, our paper includes the following main contributions:  
* A taxonomy for investigating DL-based methods to perform Visual SLAM and VOE from data acquired from RGB-D image sensors is proposed. We conducted a complete survey based on three methods to construct Visual SLAM and VOE from RGB-D images (1) using DL for modules of the Visual SLAM and VOE systems; (2) using DL to supplement the modules of Visual SLAM and VOE systems; and (3) using end-to-end DL to build Visual SLAM and VOE systems. * The surveyed studies were examined in detail and are presented in the following order: methods, evaluation dataset, evaluation measures, results, and discussion analysis.