# 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 camera pose is estimated based on the deep implicit object representation and sparsely sampled map points. [PERSON] et al. [142] proposed MID-Fusion for a multi-instance dynamic RGBD SLAM system, in which the authors used an object-level octree-based volumetric representation to estimate the camera pose in a dynamic environment. [PERSON] et al. [53] proposed a confidence-weighted adaptive network (Cowan) framework to train a depth estimation model from monocular RGB images and predict camera pose and optical flow using EgoMNet, and OFNet, respectively. Cowan's training process includes two stages: The first involves DepthNet, EgoMNet, and OFNet to predict the outputs depth map, camera pose, and optical flow, respectively. The second involves the outputs from the previous step used to filter suitable regions, allowing the network to be updated again in the previous step. [PERSON] et al. [143] proposed a method to learn neural camera pose representation coupled with neural camera movement representation in a 3D scene. The camera pose is represented by a vector, and the local camera movement is represented by a matrix operating on the vector of the camera pose. The vector representing the camera pose includes six degrees of freedom, with information such as position and direction of movement. The regression camera pose output is obtained through the DL network. [PERSON] et al. [144] proposed Objects Matter for camera relocalization in a scene; the proposed method is based on extracting object relation features and strengthening the inner representation of an image using an Object Relation Graph (ORG), where the objects in the image and the relationships between them can be important information to restore the camera pose. To extract features of objects, the proposed method uses Graph Neural Networks (GNNs) and then integrates the resulting ORG into PoseNet and MapNet to predict on many databases. To evaluate the DL module for pose estimation/camera pose estimation added to the Visual SLAM system, the studies evaluated the following datasets. SceneNet RGB-D [145] is a large synthetic database with a 5M indoor synthetics video dataset of high-quality raytraced RGB-D images, built-in full lighting conditions, and provides GT data (3D GT trajectories). The authors built a GT trajectory with a length of 5 min for one journey, with an image resolution of \(320\times 240\) pixels, resulting in 300 images in a trajectory. SceneNet RGB-D was used to evaluate semantic segmentation, instance segmentation, object detection, optical flow, camera pose estimation, and 3D scene labeling algorithms in the Visual SLAM system. The 7-Scenes dataset [146] was collected from the handheld MS Kinect RGB-D sensor with a resolution of \(640\times 480\). The GT data of the tracking camera and dense 3D model were built from the KinectFusion system based on the scene coordinate regression forest from any image pixel to points in the scene's 3D world coordinate frame. To evaluate the DL module for pose estimation added to the Visual SLAM system, the studies evaluated the following measurements. The \(ATE\) is defined in Formula (4). The Dense Correspondence Re-Projection Error (DCRE) [144] is the 2D displacement magnitude according to the 2D projection of dense 3D points rendered by 3D GT camera poses and predicted camera poses. Based on two measures \(ATE\) and DCRE, the smaller the value, the better the proposed method. The results of Visual SLAM when using the DL pose estimation module are presented in Table 7. Just like the results above, in Table 7, the results of multiple methods have been evaluated on three different datasets, so there are many empty result cells. The number of evaluation methods on the SceneNet RGB-D dataset [145] is the largest; the results show a huge difference (with the ObjectFusion_S3 [141] method the result is 0.79, but with the Maskfusion(MF)_S3 [147], result is 14.824). d. Map construction module DL  
[PERSON] et al.