# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 1 Introduction  
Localization and mapping of the environment (3D space) for robots operating in the home, autonomous vehicles in factories, and as guides for blind people are very important research in computer vision and robotics. In the study of [PERSON] et al. [1, 2], machine learning was used to train the model to predict the robot's motion trajectory and monitor the observation state. From there, applications can be developed to control the robot's two arms. These studies of Visual SLAM and VOE help entities locate themselves in the environment, understand the scene, and employ their navigation. To perform these tasks, it is necessary to solve computer vision problems. Previously, the input data used to perform the two problems Visual SLAM and VOE were SONAR sensors, 2D laser scanners, and LiDAR. In the 21 st century, the development of computer hardware and image sensors has brought many newer and more affordable types of data such as monocular, stereo, or RGB-D. They can collect visual information about their surroundings. Therefore, research on Visual SLAM and VOE in studies on these types of data is receiving very strong research attention. Recently, with the advent of DL with some popular model types such as Convolutional (conv.) Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), etc., these models have brought impressive results in computer vision, ML, and AI. Recent studies have investigated the following: [PERSON] et al. [3] conducted a survey of studies using DL for pandemic detection and prediction from 44 scientific publications on using DL for detection and prediction and related issues. The survey results were systematized and analyzed from data tables and charts in these 44 studies. At the same time, the study also clearly shows the current status and forecast results of the COVID-19 pandemic. [PERSON] et al. [4]) conducted a statistical survey and analyzed data collected from 145 stakeholders on the use of ML and AI technology in agricultural production in Shonga, Nigeria. The data collected were analyzed based on eleven factors, and shortcomings and limitations were also pointed out to find solutions to overcome these situations. [PERSON] et al. [5] conducted a study applying ML and DL to predict crimes based on hourly activities on Twitter and existing criminal profiles based on demographics. The features were extracted from the data and trained based on the SHAP model. The xgboost algorithm was used to optimize the model during the model training. The accuracy result of the model was 81%. In recent years, there have been many valuable surveys on visual SLAM and VOE, as shown in Table 1. The input data for the studies surveyed above were derived solely from image sensors, and DL was also the method, with the best results in studies on Visual SLAM and VOE. A study by [PERSON] et al. [6] presented a complete survey of Visual SLAM methods, in which the Visual SLAM construction model includes five steps (feature extraction, feature matching, pose estimation, loop closure, and map building), as shown in Figure 1. The study of [PERSON] et al. [7] is the most recent survey of Visual SLAM, in which the Visual SLAM process includes two main stages: VOE and loop closure. When broken down in detail, it includes six steps: data pre-processing, feature extraction, feature matching, pose estimation, map building, and loop closure. Recently, [PERSON] et al. [8] proposed a detailed survey of DL methods for VOE, including DL networks in each module of the VOE system and end-to-end DL for VOE. In addition, the authors also focused on the loss functions of DLs for VOE model optimization. DL is also examined with three methods to implementing Visual SLAM: adding auxiliary modules based on DL, replacing modules with DL modules, and using end-to-end DL. However, most of the above surveys are based on statistics and classification of methods and datasets without examining in detail the algorithms and results of Visual SLAM and VOE methods.