# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 6 Conclusions and Future Works  
Visual SLAM and VOE systems are often the core of control systems on autonomous vehicle systems, industrial robots, guidance systems, etc. With the advent and strong development of DL, it has brought very impressive results in solving machine learning and computer vision problems with RGB-D image input data. In this paper, we have conducted a complete survey of more than 200 studies on building Visual SLAM, VOE, and related systems. This survey is based on two main directions of Visual SLAM and VOE systems: applying DL to the steps of Visual SLAM and VOE systems and applying DL to end-to-end Visual SLAM and VOE systems. The studies have been presented in order of methods, evaluation database, evaluation measures, and experimental results. The results show that, despite receiving a lot of research attention in the past 10 years, the results on Visual SLAM and VOE are scattered according to many different criteria, because each study may only focus on one step of the Visual SLAM and VOE system.  
At the same time, we also present discussions and challenges to build Visual SLAM and VOE systems. This paper also introduced the TQU-SLAM benchmark dataset and conducted a comparative study on VOE based on fine-tuning the model with the TQU-SLAM benchmark dataset using the MLF-VO framework with backbones such as ResNet18, ResNet34, ResNet52, ResNet101, and ResNet152. Experimental studies of VOE for edge computing and assessment of difficulties and challenges in implementing DL models for VOE systems on edge devices are necessary.  
Shortly, we will test the TQU-SLAM benchmark dataset on a variety of new DL-based VOE methods. We will develop the TQU-SLAM benchmark dataset with many Visual SLAM problems such as 3D reconstruction, 3D object detection, and obstacle avoidance, as well as develop the TQU-SLAM benchmark dataset to evaluate Visual SLAM and VOE with full contexts and situations that can occur in reality when blind people move and find their way into a kitchen room. At the same time, we will try to improve the process of building moving maps and finding directions for visually impaired people in spaces  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Dividing** \\ **Cross-** \\ **Methods** \\ \end{tabular} } & \multicolumn{8}{c}{**ResNet18**} & \multicolumn{8}{c}{**Resnet54**} & \multicolumn{8}{c}{**Resnet50**} & \multicolumn{8}{c}{**Resnet101**} & \multicolumn{8}{c}{**Resnet52**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Datasets/ Methods**} & \multicolumn{1}{c}{_Erta_} & \(RMSE\) & \(ATE\) & \(Err_{d}\) & \(RMSE\) & \(ATE\) & \(Err_{a}\) & \(RMSE\) & \(ATE\) & \(Err_{a}\) & \(RMSE\) & \(ATE\) & \(Err_{a}\) & \(RMSE\) & \(ATE\) \\  & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** \\ \hline Sub1 & 19.95 & 21.67 & 28.95 & 33.70 & 36.29 & 38.93 & 34.38 & 37.14 & 34.74 & 22.38 &  
like the kitchen, especially in new environments and on the new TQU-SLAM benchmark dataset which contains more complex contexts similar to reality.  
Methodology, V.-H.L.; Writing--original draft, V.-H.L. and T.-H.-P.N.; Writing--review and editing, V.-H.L.; Visualization, V.-H.L. and T.-H.-P.N.; Supervision, V.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available in this paper.  
This research is supported by Tan Trao University in Tuyen Quang Province, Vietnam.  
The paper is our research and is not related to any organization or individual. It is part of a series of studies on visual SLAM and VO systems.