## 2 Background
### Overview of AR Placement Methods
#### 2.1.2 Marker-Less Approaches  
Under marker-less approaches, we can cite sensor-based and vision-based methods. The later can be classified into model-based and no model-based methods. The camera pose is the key parameter to connect the real and virtual worlds in both approaches, which can be used for all applications, especially in the AECO and FM (facility management) domains.  
* Sensor-based methods  
Sensor technologies (e.g., IMU: Initial Measurement Unit and GNSS) are utilized in these methods to determine the camera's location and orientation. A number of major commercial AR SDKs, including Wikitude, Vuforia, etc, supports sensor technologies for camera position estimation and tracking. In this case, inertial sensor tracking [31], acoustic tracking [32], and magnetic tracking [33] are the most usually utilized approaches in the literature.  
* Vision-Based: Model-based methods  
Most vision-based methods require a 3D model of the environment for camera pose estimation and tracking, which are called model-based tracking in computer vision [34]. Among these methods, edge-based tracking techniques require projecting a 3D geometric model (GIS or CAD, computer-aid design) on to an image and matching it with the picture's corresponding edge attributes. The 3D camera motion between frames is then computed using 2D displacement of matching characteristics [35]. The second one refers to interest-point-based or point feature methods. The core idea behind feature-point-based approaches is to extract feature points from a database of pictures and retain their positions as well as display descriptions during an offline training stage. For the template-matching, these methods use texture information in images to estimate the camera pose, but unlike interest-point-based methods, which use features, they only consider a small portion of an image, referred to as a template, to match reference images stored in an image database and a query image in the current frame of the camera at the off process [36; 37; 38]. Finally, the use of depth pictures encoding the distance of scene objects from the camera view as a pixel value is one of the most recent ways for computing the camera posture. When these depth photos and RGB images are merged, it is possible to estimate camera posture for tracking [39; 40].
* Vision-Based: No-Model-based methods  
No-model-based methods work by tracking and registering the camera phone without the need for a model or database. Such methods follow the camera's movement while concurrently constructing a 3D structure of the picture scene [41]. SFM (structure-from-motion) and SLAM (simultaneous localization and mapping) are the two main techniques for estimating the camera's pose in the AR scene. The SLAM method is developed for use in unknown environments, while the SFM method is used for known environments. We refer the reader to [42; 43] for more details about these concepts.  
Other challenges in vision-based approaches in monitoring systems exist in addition to the common problems associated with precision and operational time. One disadvantage of some vision-based approaches is their initialization stage. Many of these algorithms require manual [44] or semi-automated [45] initialization. Even if they start up automatically, they should generally start from a known point [46]. Furthermore, when a tracking pane is exposed to a fast movement or a dynamic occlusion, it is necessary to re-initialize it.