# 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
The data include 11 stereo sequences collected from slow flights under good visual conditions to dynamic flights with motion blur and poor illumination with two types of data--images collected from industrial scenarios and images collected from inside a Vicon motion capture system--with obstacles placed over the scene. To evaluate the results of the Visual SLAM algorithm using the DL module for feature extraction, the methods use several evaluation metrics as follows: (1) The absolute trajectory error (\(ATE\)) [12] is the distance error between the GT \(\hat{AT}_{i}\) and the estimated motion \(AT_{i}\) trajectory. The \(ATE\) is calculated according to Formula (4). \[ATE=\frac{1}{N}\sqrt{\sum_{i\in N}||TAT_{i}-\hat{AT}_{i}||^{2}} \tag{4}\]  
where \(N\) is the number of frames in the video used to estimate VOE. (2) \(t_{rel}\) and \(r_{rel}\) measurements: \(t_{rel}\) is the average transnational \(RMSE\) drift (%) on a length of 100-800 m. \(r_{rel}\) is the average rotational \(RMSE\) drift (\({}^{\circ}\)/100 m) on a length of 100-800 m.  
The results of Visual SLAM when using the feature extraction module using DL are shown in Table 5. The results are based on the \(ATE\), \(t_{rel}\), and \(r_{rel}\) measurements, wherein the smaller they are, the better. The results are evaluated on three databases with three types of measures, and each method only evaluates one dataset and one type of measure. Therefore, Table 5 still has many empty results. Table 5 also shows that the error results on TUM RGB-D SLAM and Euroc datasets are very low (0.03-0.5 m). The results show that RTG-SLAM [112] has the best result, with an error of 0.0106 m = 1.06 cm; SplaTAM [113] also has a very small error of 0.0339 m = 3.39 cm. These are very good results that can be applied to building practical applications in building Visual SLAM for robots and visually impaired people to find their way in indoor environments. The VO time and map construction time of SplaTAM [113] were 0.19 s/frame, and 0.33 s/frame, respectively, when performing calculations on a GPU RTX 3080 Ti, which are close to the real calculation time and can meet the requirements of practical applications. The results on the KITTI 2012 dataset are very large (8-11 m). This proves that choosing a standard dataset for evaluating the feature extraction problem also has many challenges. [PERSON] et al. [115] proposed a framework to improve the efficiency of Visual SLAM by using the results of mask-based semantic segmentation to identify feature points extraction regions (detect and segment several objects on the image). The object mask problem is implemented using DeepLab v2 [116]. This helps reduce the number of incorrect matches between correspondences when using RANSAC. The authors applied ORB-SLAM to the framework to build Visual SLAM. [PERSON] et al. [117] proposed DS-SLAM to improve localization efficiency in dynamic environments when performing pose estimation. DS-SLAM has five threads running in parallel: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. In particular, the local mapping thread and loop closing thread are implemented similarly to ORB-SLAM. DS-SLAM uses the raw RGB image is utilized to semantic segmentation and moving consistency check simultaneously via SegNet and RANSAC, respectively. Finally, the global octo-tree map is built based on the combination of the local point clouds created from the keyframes' transform matrix and the depth images.