# 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 l c c c c c} \hline \hline \multirow{3}{*}{**Authors/Years**} & \multirow{3}{*}{\begin{tabular}{c} **Dataset/** \\ **Measu/** \\ **Methods** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **TUM RGB-D** \\ **SLAM** \\ **Dataset** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **KITTI 2012** \\ **Dataset** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Eurco** \\ **Dataset** \\ \end{tabular} } \\  & & \multicolumn{1}{c}{\begin{tabular}{c} **ATE (m)** \\ **(s)** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **t\({}_{std}\) (v\({}_{s}\))** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **t\({}_{std}\) (deg/100 m)** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} _ATE (m)_ \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} _ATE (m)_ \\ \end{tabular} } \\ \hline
[118]/2017 & ORB-SLAM2 & & & 0.727 & 0.22 & - & - \\ \hline
[119]/2019 & GCN-SLAM & 0.05 & - & - & - & - \\ \hline
[110]/2020 & SP-Flow & & & & & \\ SLAM & 0.03 & - & - & - & - \\ \hline
[110]/2020 & Stereo & & & & & \\ LSD-SLAM & - & 0.942 & 0.272 & - & - \\ \hline
[110]/2020 & SP-Flow & & & & & \\ SLAM(stereo) & - & 0.76 & 0.19 & - & - \\ \hline
[111]/2020 & LIFT-SLAM & - & - & - & 9.19 & 0.573 \\ \hline
[111]/2021 &
\begin{tabular}{c} LIFT-SLAM \\ (fine-tune KIT) \\ \end{tabular} & - & - & - & 11.33 & 0.08 \\ \hline
[111]/2021 &
\begin{tabular}{c} LIFT-SLAM \\ (fine-tune Euroc) \\ \end{tabular} & - & - & - & 8.94 & 0.07 \\ \hline
[111]/2021 &
\begin{tabular}{c} Adaptive \\ LIFT-SLAM \\ (fine-tune KIT) \\ \end{tabular} & - & - & - & 8.56 & 0.04 \\ \hline
[111]/2021 &
\begin{tabular}{c} Adaptive \\ LIFT-SLAM \\ (fine-tune Euroc) \\ \end{tabular} & - & - & - & 11.24 & 0.28 \\ \hline
[111]/2021 &
\begin{tabular}{c} Adaptive \\ LIFT-SLAM \\ (fine-tune Euroc) \\ \end{tabular} & - & - & - & 11.3 & 0.048 \\ \hline
[120]/2023 & Point-SLAM & 0.0892 & - & - & - & - \\ \hline
[121]/2023 & ESLAM & 0.0211 & - & - & - & - \\ \hline
[122]/2023 & Co-SLAM & 0.0274 & - & - & - & - \\ \hline
[113]/2024 & SpaTAM & 0.0339 & - & - & - & - \\ \hline
[112]/2024 & RTG-SLAM & 0.0106 & - & - & - & - \\ \hline
[123]/2024 &
\begin{tabular}{c} SG-Init+ \\ DROD(L) \\ \end{tabular} & - & - & 9.07 & - \\ \hline
[123]/2024 &
\begin{tabular}{c} SG-Init+ \\ DRODID (N/A) \\ \end{tabular} & - & - & 14.92 & - \\ \hline
[124]/2024 &
\begin{tabular}{c} LGU-VO \\ \end{tabular} & - & - & & 0.139 \\ \hline
[124]/2024 &
\begin{tabular}{c} LGU-SLAM \\ (w/o 5 SL) \\ \end{tabular} & - & - & & 0.142 \\ \hline
[124]/2024 &
\begin{tabular}{c} LGU (w/o 5M) \\ \end{tabular} & - & - & & 0.146 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Visual SLAM when using the feature extraction module using DL. [PERSON] et al. [125] proposed DynaSLAM based on ORB-SLAM2. DynaSLAM's input data are in dynamic scenarios for monocular, stereo, and RGB-D images. DynaSLAM can detect moving objects using multi-view geometry, DL, or both types of models. The pixel-wise semantic segmentation of dynamic objects with stereo and monocular input data is performed using Mask R-CNN, with RGB-D data using the multi-view geometry method for rendering. The mapping and tracking steps are performed based on ORB-SLAM2. [PERSON] et al. [126] proposed Detect-SLAM based on integrating ORB-SLAM2, which involves an object detection module using a Single-Shot Multi-box Object Detector (SSD). Detect-SLAM also includes three parallel streams: tracking, local mapping, and loop closing. However, there are the following new points: First, Detect-SLAM only cares about moving objects. The second is that the static objects are reconstructed on keyframes according to the point cloud data and the object map is also constructed. The third is to improve object detection results using a SLAM-enhanced detector. [PERSON] et al. [127] proposed a novel framework for the Visual SLAM system based on the combination of Faster RCNN for object detection, semantic segmentation in 3D space, and the estimation results from the SLAM system.