## 3 Types of Augmented Reality Applications
### Markerless AR  
Also known as location-based AR [22], markerless augmented reality applications are a type of AR that doesn't require a predefined marker to overlay digital content in the real world. Instead, these applications use features in the environment, such as surfaces,patterns, or objects, or leverage technologies like GPS, accelerometer data, or computer vision techniques to position the AR content [23]. The exact way this type of AR technology works can vary depending on the specific application [22], for example:  
* **AR Navigation Apps:** AR navigation apps typically use a combination of GPS, accelerometer, gyroscope, and sometimes compass data from the user's device to determine the location and orientation in the real world. This data is combined with computer vision techniques to recognize landmarks or features in the environment. For instance, Google's Live View feature in Google Maps uses machine learning to recognize buildings and landmarks from its Street View data. This combination of data allows the application to accurately overlay directions in the user's view of the real world [24]. * **AR Games:** AR games like Pokemon Go use the device's GPS to determine the player's location and then spawn digital creatures in the real world at those coordinates. The device's camera captures the real world, and the game overlays the digital creatures onto this view. The game uses data from the device's accelerometer and gyroscope to adjust the view and orientation of the digital creatures as the players move their device [22]. * **AR Funiture Apps:** These apps use computer vision techniques to identify and track flat surfaces in the user's environment. The process typically involves feature detection and extraction, where key points in the image are identified. These features are then tracked across multiple frames to estimate the motion of the camera and create a 3D representation of the environment. Once a flat surface has been identified, the app can overlay a 3D model of a piece of furniture onto that surface [24]. * **AR Social Media Filters:** These applications use facial recognition and tracking to overlay digital filters onto users' faces. The process typically involves first detecting the face in the image, often using machine learning models. Once the face is detected, key facial features such as the eyes, nose, and mouth are identified. The position of these features is then tracked in real-time as the user moves. The digital filter is adjusted based on these movements to ensure it aligns correctly with the user's face [24]. * **AR Maintenance and Repair:** These applications can use object recognition to identify specific components in a machine or system. This often involves training a machine learning model on images of the components so it can recognize them in the real world. Once a component is recognized, the application can overlay digital instructions or diagrams onto the component to guide the user through the repair process [25]. Alternatively, new sectors have encouraged the use of markerless AR technologies, such as in [13] where the author presents an outdoor markerless AR mobile application for tourism. The application provides tourists with AR information overlays related to points of interest, such as historical sites or landmarks, using GPS and sensor data to position the AR content. An algorithm based on Natural Feature Tracking (NFT) to create a more immersive and accurate AR experience is used. The application captures images of the environment using the device's camera and then extracts feature points from the images. These feature points are matched with pre-stored points in a database, allowing the app to accurately position the AR content, enhancing the overall experience for tourists [13]. Markerless AR has many advantages over marker-based AR, including greater flexibility and a more immersive experience.