# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 2 Related Work  
Visual SLAM and VOE surveys are not new. In just six years, we found 18 valuable papers on Visual SLAM and VOE surveys [6, 7, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]. Details of these survey studies are shown in Table 1. Therein, the research of [PERSON] et al. [14] surveyed Visual SLAM studies from 2010 to 2016 based on traditional machine learning methods and their results, though DL-based studies were not presented. [PERSON] et al. [15] conducted a survey study on collecting data databases from monocular cameras (KITTI, EuRoC, TUM, ADVIO, and monocular visual-inertial SLAM) for evaluating traditional machine learning-based Visual SLAM models such as the PTAM, ORB-SLAM2, LSD-SLAM, and DSO. [PERSON] et al. [16] performed a small survey for a dynamic SLAM system based on deep learning, and some comparisons of traditional DL and ML are briefly presented. [PERSON] et al. [17] conducted a survey on Visual SLAM based on extracted features; the surveyed features are traditional features, including low-level features, mid-level features, high-level features, and hybrid features. These features were also only applied to traditional machine learning models. [PERSON] et al. [18] surveyed a semantic SLAM system oriented to building applications for moving robots. The features extracted to solve each step in the Visual SLAM model were extracted from DL. However, this study only surveyed up to 2019, and the discussions were mainly about the methods of implementing Visual SLAM; the results were not presented in detail. [PERSON] et al. [19] performed a comparative study was conducted to estimate VOE on the TUM RGB-D SLAM [12] database, in which features for VOE were extracted based on Mask CNN for detecting and segmenting objects in the scene. [PERSON] et al. [20] surveyed Visual SLAM in three aspects: visual only, visual-inertial, and RGB-D SLAM. The surveyed methods included both traditional machine learning and DL-based methods tested on commonly used databases such as the KITTI [9, 10, 11] dataset, TUM RGB-D SLAM [12] dataset, ICL-NUIM [13] dataset, etc. The surveyed problem was limited to the methods; detailed results have not been presented. [PERSON] et al. [6] conducted a survey study on the state-of-the-art methods for building Visual SLAM; the survey was conducted from the technology of data collection devices to traditional feature extraction methods to DL feature extraction methods. The methods for building Visual SLAM were also surveyed from traditional machine learning to DL. However, the problems were surveyed based on the method only, and the results were not presented in detail. [PERSON] et al. [21] conducted a survey study on underwater Visual SLAM construction methods. The research was based on two feature extraction methods, geometry-based and DL-based, for estimating underwater movement direction and position. [PERSON] et al. [22] conducted a small survey on Visual SLAM methods in the traditional model, including steps when building Visual SLAM such as feature detection and matching, key frame selection, closed-loop detection, and map optimization, and some outstanding studies on Visual SLAM up to 2022 were also introduced. [PERSON] et al. [23] conducted a survey study on the use of loop closure detection in Visual SLAM methods over the past 20 years to 2022. The studies only stopped at introducing the methods and analyzing some advantages and disadvantages; results not presented in detail.Chen et al. [24] conducted a survey study on the development process of semantic Visual SLAM and three problems of semantic Visual SLAM, which are semantic information extraction on the images, semantic object association, and the application of extracted semantic information. At the same time, commonly used databases for VisualSLAM were analyzed and compared. [PERSON] et al. [25] surveyed Visual SLAM methods from data collected from UAVs up to 2022. Detailed results were not presented in this study. [PERSON] et al.