## 2 Related Work  
Multiple works demonstrating the benefits of AR technology as a tool for maintenance in the industrial sector have been published [1; 2; 19]. For example, [PERSON] et al. [20] showed that using AR can reduce errors by 92%, while maintenance tasks were performed 79% faster. Within the aeronautical sector, [PERSON] et al. [21] estimated a 27% reduction in time when carrying out the assembly and the disassembly of an airplane within a maintenance operation using AR. In [22], the authors showed how AR technology can be also used to train technicians to carry out maintenance tasks. Most of these studies have highlighted the importance of the tracking method chosen as one of the determining factors to guarantee the success of the AR system applied to maintenance [2; 7]. Traditionally, the use of fiducial external 2D markers, such as ARToolkit or ArUco, has been the tracking method most used by the scientific community to add virtual elements in AR systems applied to maintenance, assembly, and repair in the industrial sector [23]. This is because it is relatively easy to quickly and accurately identify and estimate the pose of these markers using computer vision techniques [7; 24]. Despite its extensive use in the industry, this type of tracking technique has some important limitations [2; 7; 20; 24]: The technician must spend a lot of time manually setting the physical fiducial markers, to correctly align the virtual content of the scene. At the same time, multiple markers must be used and related to each other, such that the AR system is able to estimate the pose from any point of view of an object [24]. Secondly, the technician's ability to work in real situations is reduced, as the markers may interfere with access to objects of interest [7; 24]. Moreover, these types of markers are critically affected when the marker is occluded or under non-controlled light conditions [7; 24]. The aforementioned technical limitations make it difficult to apply these AR systems to maintenance in the industrial sector and, in particular, for performing on-site maintenance tasks. As an alternative to marker-based tracking systems, markerless AR systems do not need any kind of additional marker scene to accurately add and align virtual information about any element of the scene [24]. These methods use the natural features of the scene, such as texture (e.g., a color histogram) or geometric (e.g., edges) information to perform tracking. In Table 1 are summarized the main AR methods for AR tracking. For example, some of the most common tracking methods in AR use Simultaneous Localization and Mapping (SLAM) techniques, such as the PTAM [25] method. These methods simultaneously estimate the pose of the camera in real time, with respect to an unknown scene, while performing the 3D reconstruction of it. The main limitation of this kind of method is that they usually produce failures when the scene is dynamic, and it is required that the scene is sufficiently textured to estimate key points. Thus, they are generally not suitable for maintenance tasks, as the environment may be dynamic and industrial environments are usually not sufficiently textured [7]. Another type of tracking method is those known as model-based. These methods estimate the pose of a real 3D object from information obtained from a 3D or CAD model of the object. These methods can, in turn, be divided into two types: On one hand, tracking by detection methods detect and estimate the pose of the 3D object independently in each frame of a video sequence; on the other hand, 6 DOF pose tracking methods start from an initial pose of the object and update its pose throughout the successive frames of the video sequence (i.e., frame-to-frame tracking). Specifically, 6 DOF pose tracking methods minimize the error between the contour of the observed object and its model using an iterative optimization approach [17; 28; 29; 30]. Although these methods are usually fast, the main limitation is that they require an initial pose of the object [29].