# 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
[136] proposed DM-SLAM based on feature-based methods to improve the results of the location accuracy in dynamic environments. DM-SLAM is combined from an instance segmentation network with optical flow information. DM-SLAM includes four modules: semantic segmentation, ego-motion estimation, dynamic point detection, and a feature-based SLAM framework. The semantic segmentation module uses Mask R-CNN for object segment segmentation, followed by moving points being detected and removed in ego-motion estimation. The dynamic feature points are extracted from the dynamic point region detected in the previous step, and finally, the feature-based SLAM framework module uses ORB-SLAM2. [PERSON] et al. [137] proposed RDS-SLAM to improve the results of building Visual SLAM systems in real-world dynamic environments. RDS-SLAM proposes a semantic segmentation thread that does not have to wait for results from any module, and the tracking thread also does not have to wait for results from the segmentation module. This method helps to effectively perform semantic segmentation results for dynamic object detection and eliminate outliers. The next implementation of RDS-SLAM is based on ORB-SLAM3. [PERSON] et al. [138] proposed a real-time visual SLAM algorithm based on deep learning based on ORB-SLAM2. To extract semantic information in images, a parallel semantic thread is built. To remove dynamic features in the image, the authors used an optimized optical flow mask module. Dynamic objects in images are detected using YOLOv5s built into the semantic thread. To improve the system results in the tracking module, a method of optimizing the homograph matrix is used. To evaluate the DL module for semantic segmentation added to the Visual SLAM system, the below studies evaluated the following datasets. CARLA [139] was used to study the results of three approaches to autonomous driving: a classic modular pipeline, an end-to-end model trained via imitation learning, and an end-to-end model trained via reinforcement learning. CARLA can provide automated digital environments such as urban layouts, buildings, and vehicles. This can support the development, training, and validation of urban automated driving systems. ObjectFusion dataset I, ObjectFusion dataset II, ObjectFusion dataset III, and ObjectFusion dataset VI [127] were collected from the Asus Xtion Pro RGB-D sensor and in indoor environments. Trajectories were chosen to build data with many prominent objects as keyframes both locally and globally. ObjectFusion dataset I involves an object in each frame of the scene; the data are a frame sequence consisting of 1801 frames. ObjectFusion dataset II involves multiple objects in each frame of the scene such as chairs, dogs, pot plants, and so on; the data were collected in full lighting conditions and consist of a frame sequence consisting of 1625 frames. ObjectFusion dataset III was collected in a more challenging context; data were collected in a scene with many objects that are occluded in many frames. ObjectFusion dataset VI is similar to ObjectFusion dataset III, and the data were captured in a scene with many objects and moving obstacles. The ICL-NUIM dataset [13] is an RGB-D benchmarking used to evaluate system VOE and visual SLAM algorithms. Image data compiled from camera trajectories in raytraced 3D models in POVRay with two scenes in the living room and office provide some GT data. Living room GT data include 3D surface GTs, together with the depth maps, camera poses, and camera trajectory, in addition to original data for 3D reconstruction evaluation. The ADVIO dataset [38] was collected from an iPhone, a Google Pixel Android phone, and a Google Tango device in different indoor and outdoor scenes with 23 sequence frames (7 sequences collected from office indoor scenes, 12 sequences collected from urban indoor scenes, 2 sequences collected from urban outdoor scenes, and 2 sequences collected from suburban outdoor scenes).