# 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 input data of the framework are obtained as an RGB-D image. First, the local target map is built using CNN to detect the 2D object proposals. Then, the dynamic global target map is updated based on the local target map obtained by CNNs. Finally, the detection result of the current frame is obtained by projecting the global target map into 2D space. [PERSON] et al. [128] proposed OFB-SLAM to improve the results of the Visual SLAM system in the case of a dynamic environment. OFB-SLAM uses optical flow in a feature-based monocular SLAM system to remove dynamic feature points on the input frame. It includes two modules: ego-motion estimation and dynamic feature points detection. The ego-motion estimation module extracts the feature points from the current frame and the previous frame; to find the corresponding feature pair between the two frames, RANSAC is used. Optical flow is used to detect object motion. OFB-SLAM is integrated into ORB-SLAM and implements the next steps of the Visual SLAM system. [PERSON] et al. [129] proposed a method to filter outliers of RANSAC-based F-matrix calculations using faster R-CNN. Therein, the inliers are trained using semantic patches tailored which can provide semantic labels of image regions. From there, low-quality feature areas are effectively reduced. The proposed method is added to the ORB-SLAM system. [PERSON] et al. [130] proposed Deep SAFT to improve feature-based vSLAM's applicability in more challenging environmental conditions. Deep SAFT is an online learning scene adaptation feature transform that is capable of self-adapting to recently observed scenes by taking advantage of the advantages of CNN. The authors used Deep SAFT to replace ORB-SLAM2 in the Visual SLAM system. [PERSON] et al. [131] proposed the Edge-Feature Razor (EF-Razor) method. EF-Razor first uses semantic information offered by the real-time object detection method YOLOv3 to distinguish edge features. To effectively filter unstable features onto the SLAM system, EF-Razor was used. The authors integrated EF-Razor into ORB-SLAM2. [PERSON] et al. [132] proposed a semantic SLAM using method objects and walls as a model of an environment, called RoomSLAM. RoomSLAM includes two modules running in parallel: front-end and back-end. The front-end performs object detection and wall detection using YOLOv3 on RGB images, and depth images are converted to point cloud data to determine the location of objects and walls in the 3D space of the real world. They are seen as landmarks of the environment, and the walls are used to construct rooms in the scene. The back-end is responsible for estimating the state through the optimization graph. In RoomSLAM, a second component that is also very important is the room. RoomSLAM also looks for similarities between rooms to detect loop closures. [PERSON] et al. [133] proposed an UnsupervisedSemantic Segmentation SLAM framework, called USS-SLAM, to improve robot positioning accuracy when moving. This framework is integrated into ORB-SLAM2. To do this, USS-SLAM filters out dynamic features using a semantic segmentation model learned from the DeepLab V2 unsupervised learning network, whose backbone is ResNet. This learning method can be trained by the adversarial transfer learning method in multi-level feature spaces. The next steps of the Visual SLAM system are based on ORB-SLAM2. [PERSON] et al. [134] proposed a semantic visual-inertial SLAM system for dynamic environments based on VINS-Mono [135] with three streams: RGB-image manager, semantic segmentation manager, and feature point processing. In particular, RGB-image manager and semantic segmentation manager use the RGB-images and the semantic segmentation result. The feature point processing flow uses the optical flow to track feature points on the RGB frames. The result of this research is that it is possible to perform real-time trajectory estimation by utilizing the pixel-wise results of semantic segmentation. [PERSON] et al.