## 2 Background  
The term \"placement\" or \"placing a model\" has not a common definition in the literature. This concept was used by some authors [15; 16; 17], who defined it as the positioning of a model to be enhanced on location. Several references [12; 13; 14; 18] as well as Microsoft have used the word \"Alignment\" to describe how the augmented reality application \"SnapToReality\" performed during testing. The placement of virtual items in the real world is also referred to as \"overlaying\" [19; 20]. In the present paper, the term \"Placement\" is adopted.  
According to the literature, placement via AR is the superposition of virtual 3D models and elements at the appropriate, predetermined locations in the real world in a precise and constant way. As a result, a posture estimation method, or more specifically, a camera localization method, is correlated to the solution to this problem [20]. According to [PERSON] (2009), understanding the location of the camera in reference to the actual scene is necessary for properly positioning virtual items in relation to real-world objects.  
The placement of AR information in the real world requires three qualities to achieve an efficient overlay between the virtual and the real environment: placement accuracy; real-time interactivity; and 3D tracking [18; 21]. The first step is to establish a link between the physical and virtual worlds. Then, to augment virtual information on real objects, their location must first be identified in 3D space and in real time for the camera. This is known as 3D tracking, and it entails estimating camera poses with six degrees of freedom (6-DoF): three components for position and three components for orientation. 6-DoF refers to the object's movement in 3D space along the \(X\), \(Y\), and \(Z\) axes, as well as rotation along the pitch, yaw and roll axis. As a result, virtual objects in the camera's field of view can be identified by matching the camera's position to a previously developed 3D model of the environment when the pose camera is estimated. After that, virtual and real-world objects are superimposed.  
One of the most critical issues in AR is to ensure that the placement process is accurate, in real time, and stable [22]. This issue has been addressed by several authors. Ref. [19] studied placement methods by referencing two error types. The first one is the static faults, which include sensor measurement inaccuracies, mechanical misalignments between sensors, and incorrect registration algorithm. The second form of errors is dynamic; it includes time delays between the occurrence of an actual event and its arrival to the host, as well as timing delays. It should be also noted that hardware is critical to the AR system's advancement because it may enhance the tracking, data storage, and placement of the virtual and real worlds [23].  
Several approaches to the placement of virtual objects in a real environment have been proposed in the literature, including vision-based, hybrid, and sensor-based approaches [21; 24]. Figure 1 below proposes a taxonomy of these approaches based on the adopted method and the requirements to prepare the environment before usage. Another classification distinguishes two families of methods: marker-based and marker-less methods [25]. In the next sub-sections below, we give an overview of the aforementioned methods by highlighting their advantages and limits as well as their influencing factors.