## 4 Overview of Included Studies
These limitations, combined with the system's lack of scalability and highly specific use case, suggest that further development would be needed before this approach could be considered for practical metaverse applications. Having provided a thorough review of related papers, a comprehensive table that summarizes the details of each reviewed paper is presented in Table 1, aiming to provide a clear and concise summary of various studies, highlighting their contributions and limitations as well as providing technical info such as tools and methodologies used. As mentioned in Section 3, to increase the inclusivity, objectivity and comprehensiveness of this systematic review, we have additionally included an overview of related articles selected from the databases of ACM and IEEE. These articles are within the scope of our paper, and adhere to the inclusion criteria. A representative set of 3 articles from each database was selected based on their impact and recency. Starting with the first article selected from ACM database where authors of [80] created a safety control system with the integration of DT technology as well as IoT and AI to enhance human-robot-collaboration (HRC) and safety in industrial settings by calculating the appropriate distance between humans and robots. In the design phase VR is employed to create test DTs and identify potential safety hazards in HRC scenarios based on feedback from real-world IoT sensors in the physical system. This enables for two-way iterative optimization of the physical and the virtual system. In the production phase, CNNs are used to monitor and calculate the safety distance between humans and robots. The layered design includes applications for feedback, robot planning, and real-time DT data exchange. Binocular cameras were used for real-time monitoring and a Cascade Pyramid Network (which contains 2 sub-networks: GlobalNet and RefineNet) was used for human key point detection. The authors also developed a proprietary algorithm to calculate the minimum safety distance. The system achieved a 97.25% accuracy in label recognition after testing with a robotic arm in an automobile assembly station, with Unity3D used for VR simulation. A programmable logic controller (PLC) was used to control the robotic arm and the communication between the VR system and the PLC was established through the OPC Unified Architecture (OPC UA) protocol. The UDP protocol was used for real-time data streaming and Web services. Some of the system's limitations is its reliance on pre-defined scenarios and the lack of consideration for the detection of environmental obstacles apart from humans. Additionally, the dataset of 1000 images mentioned to train the model and the 80-20 split between the training and testing set limits the model's generalizability. Further testing with a wider audience and devices to expand the dataset will help improve the system's performance in real life settings. Regarding the decrease of the response time, the authors emphasize the need for the integration of advanced technologies such as 5G and cloud computing. \begin{table}
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In [81] a telexistence UAV system was developed for 3D urban reconstruction, improving post-processing time and reducing 3D scan uncertainties. The system combines a UAV equipped with a stereo camera and an NVIDIA Jetson TX2 computing unit for image capture and data processing along with an HMD for real-time visualization. The system employed visual-inertial odometry (VIO) for robust camera tracking and combined it with a depth-fusion framework (a technique that combines depth data from multiple frames to create a consistent 3D model) to reduce pose drift and improve model accuracy. The depth fusion framework was enhanced by incorporating inertial measurement unit (IMU) data. The UAV captures IMU and image data, processes it on the compute unit and transmits it via Wi-Fi to the ground station. There the geometry data and maps motion is reconstructed based on the HMD user's head pose.