# 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  
a. Feature extraction module DL  
[PERSON] et al. [110] proposed a keypoint extraction network used to resemble the Oriented FAST and Rotated BRIEF (ORB)-SLAM2 module in VOE. Therefore, SP-Flow is used to replace ORB-SLAM2 in VOE construction. This network is called SP-Flow. It is a combination of a self-supervised framework and the Lucas-Kanade method. The self-supervised framework of SP-Flow includes three stages: keypoint pre-training, keypoint self-labeling, and joint training. In the Visual SLAM model, whether feature extraction is effective often depends on the feature point extraction for a single image and its feature point matching accuracy between two successive frames. SP-Flow has tried to simplify the \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Authors/Years**} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multicolumn{2}{c}{**Oxford**} & \multicolumn{2}{c}{**EF**} & \multicolumn{2}{c}{\begin{tabular}{} \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{} \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{} \end{tabular} } \\ \cline{2-10}  & & & & & & & & & \\ \cline{1-1} \cline{6-10}
[04]/2004 & SIFT & 20.7 & 46.5 & 43.6 & 32.2 & 23 & 0.296 & 0.49 & 0.494 \\ \cline{1-1}
[01]/2006 & Fast & 26.4 & 53.8 & 47.9 & 39 & 28 & - & - & - \\ \cline{1-1}
[05]/2006 & SURF & 29.9 & 56.9 & 57.6 & 43.6 & 28.7 & 0.235 & 0.493 & 0.481 \\ \cline{1-1}
[02]/2009 & SPOP & 22.9 & 51.3 & 39.3 & 42.2 & 21.2 & - & - & - \\ \cline{1-1}
[94]/2011 & EdgeFoci & 30 & 54.9 & 47.5 & 46.2 & 31 & - & - & - \\ \cline{1-1}  
FPoint & - & - & - & - & - & 0.617 & 0.678 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The keypoint detection and feature matching results based on DL. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Datasets/ Authors/ Authors/ Authors/ Authors/ Years**} & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{} \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{} \end{tabular}  } & \multicolumn{2}{c}{\ \begin{tabular}{} \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{} \end{tabular}  } \\ \cline{2-10}  & & & & & & & & & & \\ \cline{1-1}
[83] & & & & & & & & & & & \\ \cline{1-1}
[03] & & & & & & & & & & & \\ \cline{1-1}
[04]/2004 & SIFT & 20.7 & 46.5 & 43.6 & 32.2 & 23 & 0.296 & 0.49 & 0.494 \\ \cline{1-1}
[101]/2006 & Fast & 26.4 & 53.8 & 47.9 & 39 & 28 & - & - & - \\ \cline{1-1}
[105]/2006 & SURF & 29.9 & 56.9 & 57.6 & 43.6 & 28.7 & 0.235 & 0.493 & 0.4  
feature extraction process but still ensures accuracy. The architecture of SP-Flow includes six conventional convolution layers. [PERSON] et al. [111] proposed a module based on the Learned Invariant Feature Transform (LIFT) in the traditional ORB-SLAM Visual SLAM construction method. This module is responsible for extracting features from images for ORB-SLAM. The architecture of LIFT is based on a CNN consisting of three modules: detector, orientation estimator, and descriptor. It is a very important module in ORB-SLAM, and LIFT has been pre-trained on many VOE datasets. Studies based on this approach have performed evaluations on databases such as the TUM RGB-D SLAM dataset [12], the KITTI 2012 dataset [9], and the Euroc dataset [36]. The TUM RGB-D SLAM [12] and the KITTI 2012 [9] datasets have been presented above. The Euroc dataset [36] was collected onboard a Micro Aerial Vehicle (MAV) based on the stereo camera and synchronized IMU measurements. This dataset has been used to evaluate the visual-inertial SLAM and 3D reconstruction capabilities.