### Augmented Reality (AR)
Depending on the selection of devices, the overlaying augmentations technique onto the user's scene can be different. Currently, there are three superimposing techniques. With the first technique, the augmentation can be directly projected to the field of view (FoV) of the user. This one is called optical combination and is implemented with an optical see-through HMD (OST-HMD). The second technique is known as video mixing. The user's scene is taken by the camera and processed by a computer. After inserting the augmentations on the processed scene, the result is displayed on a display device, on which the user views the real scene indirectly. The last technique is image projection, which directly projects the augmentations onto the physical objects. Tracking and registration are the crucial and challenging aspects of AR applications. The accuracy in tracking and registration determines the alignment quality of augmentations. According to [18], the tracking and registration algorithms are divided into three groups: (1) Marker-based algorithms, (2) Markerless (or Natural feature-based) algorithms and (3) Model-based algorithms. For marker-based tracking, the 2D markers having unique shapes/patterns are placed on the real objects, where the digital information is planned to be overlayed. A digital augmentation is programmatically assigned for each marker in the workplace. When the camera recognizes the markers, the pre-assigned augmentation is displayed onto the marker. In some situations, the markers are occluded and it is not efficient. Thus, natural feature-based tracking (NFT) is more commonly used in computer vision-based tracking. Some well-known natural feature-based tracking algorithms are Speeded Up Robust Features (SURF), Scale Invariant Feature Transform (SIFT) and Binary Robust Independent Elementary Features (BRIEF). This technique extracts the characteristic points in images to train the AR system's real-time detection of those points. Despite providing the seamless integration of augmentations into the real world, natural feature tracking (NFT) intensively depends on computational power and is slower and less effective at long distances. Therefore, small artificial markers knowns as fiducial markers are used to mitigate the disadvantages of NFT by accelerating the initial recognition, decreasing the computational requirements and improving the system's performance. Model-based tracking algorithms utilize a predefined list of models, which are then compared with the real-time extracted features. In general, a basic pipeline of AR system/application consists of image capturing, digital image processing, tracking, interaction handling, information management, rendering and displaying [17]. It starts by capturing a frame with the device's camera. Then comes the Digital Image Processing step of AR software to process the captured image in order to estimate the camera position in relation to a reference point/object (a marker, an optical target, etc.). This estimation can also utilize the internal sensors, which help in tracking the reference object. The camera positioning accuracy is crucial for displaying AR content because it needs to be scaled and rotated according to the scenarios. After that,the processed image is rendered for the relevant perspective and is shown to the user on a display device. In some cases, when certain remote or local information is required, the Information Management module is responsible for accessing it. The interaction handling module is to enable the users' interaction with the image.