# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
## 2 Related Work
[26] surveyed the development stages of Visual SLAM construction methods up to 2022 regarding data collection device technology, databases, and Visual SLAM methods. However, detailed results were not presented. [PERSON] et al. [27] conducted a survey on VOE based on LiDAR, GPS/IMU, and image sensor data. The surveyed methods range from traditional to DL methods. [PERSON] et al. [28] have done a fairly comprehensive survey on Visual SLAM up to 2022. The survey research ranges from data types collected from different sensors such as LiDAR, GPS, and image sensors to traditional methods and DL. [PERSON] et al. [29] conducted a fairly comprehensive survey on DL-based Visual SLAM up to 2022 according to the mechanisms of machine learning: unsupervised learning, self-supervised learning, and supervised learning, especially the methods that are analyzed for advantages and disadvantages. However, the specific results were not presented. [PERSON] et al. [7] conducted a comprehensive survey on Visual SLAM and DL-based VOE up to 2023. The studies were surveyed in detail up to the year, main approaches, and evaluation databases. However, detailed results were not presented. [PERSON] et al. [30] conducted a comprehensive survey on Visual SLAM and VOE based on traditional features and features extracted from DL. Regarding the Visual SLAM categories, refs. [28; 29] have done a very valuable survey of DL techniques for Visual SLAM. In the research of [29], the authors proposed a taxonomy of four DL-based learning methods: modular learning, joint learning, confidence learning, and active learning. Modular learning includes learning depth to estimate the depth of the scene; learning optical flow is the process of determining optical flow (the process of determining the movement of a camera or object in the scene). [PERSON] et al. [20] surveyed Visual SLAM algorithms, including three approaches based on output data: visual-only SLAM, visual-inertial SLAM, and RGB-D SLAM. For each method, a timeline is presented. Finally, databases for evaluating Visual SLAM algorithms are presented. In more detail, research by [24] surveyed semantic Visual SLAM. The survey is based on semantic Visual SLAM construction that meets the requirements of accuracy and real-time application. The authors investigated three methods--object detection, semantic segmentation, and instance segmentation--to extract semantic information from the environment. [PERSON] et al. [15] conducted a survey and evaluated the algorithms of visual-inertial SLAM. The basic theories of Visual SLAM and visual-inertial SLAM are presented. The most important types of content include filtering-based methods and optimization-based methods presented to solve the problem of building Visual SLAM and visual-inertial SLAM systems. Finally, databases KITTI [10], EuRoC [36], TUM VI [37], ADVIO [38], and VICON [15] are listed and were used to evaluate visual-inertial SLAM construction models. [PERSON] et al. [26] presented a survey based on 45 recent outstanding studies on Visual SLAM, in which recent advancements and impressive results of Visual SLAM were analyzed and discussed based on the novelty domain, objectives, employing algorithms, and semantic level. At the same time, the existing challenges and trends of Visual SLAM systems were discussed. [PERSON] et al. [7] presented state-of-the-art Visual SLAM systems, in which the Visual SLAM system construction model was also surveyed and presented with a very detailed approach to using DL techniques. At the same time, prominent databases for evaluating Visual SLAM models were also listed and briefly described. Exmining only VOE categories, [PERSON] et al. [27] conducted a complete and detailed survey of VOE systems used for robots and autonomous vehicles operating indoors. The authors presented the state of the art of VOE from models, algorithms, and results. The results showed an increase in accuracy of 33.14% for trajectory construction from point cloud data.