###### Abstract  
In recent years, the benefits of both Augmented Reality (AR) technology and infrared thermography (IRT) have been demonstrated in the industrial maintenance sector, allowing maintenance operations to be carried out in a safer, faster, and more efficient manner. However, there still exists no solution that optimally combines both technologies. In this work, we propose a new AR system--MANTRA--with specific application to industrial maintenance. The system can automatically align virtual information and temperature on any 3D object, in real time. This is achieved through the joint use of an RGB-D sensor and an IRT camera, leading to high accuracy and robustness. To achieve this objective, a pose estimation method that combines a deep-learning-based object detection method, YOLOV4, together with the template-based LINEMOD pose estimation method, as well as a model-based 6 DOF pose tracking technique, was developed. The MANTRA system is validated both quantitatively and qualitatively through a real use-case, demonstrating the effectiveness of the system compared to traditional methods and those using only AR. Augmented Reality; thermography; maintenance; tracking; Industry 4.0 202111385. 20211385. 20211385. 20211385. 20211385. 20211385. 20211385. 2021385.  
production cost [6]. Moreover, in sectors such as aeronautics, maintenance and reparation tasks can increase to up to 80% of the total product cost during its life cycle [1]. An AR system geometrically aligns virtual and real objects in the real world in real time, to \"augment\" the available information [2]. This alignment requires the estimation of the scene-relative position and orientation of the camera. This process is called tracking [1,2]. In particular, when the position and orientation of a specific object in the scene are required to be known, it is called object pose estimation. There are multiple ways of solving tracking problems, which can mostly be divided into two types: Using external markers or without them (markerless tracking). On one hand, marker-based tracking techniques employ tags placed manually over the scene to estimate the camera pose. On the other hand, markerless tracking techniques use the natural features of the scene to estimate it. Industrial maintenance objects tend to have the following attributes: They are complex and textureless 3D objects. Moreover, they usually have partial occlusion, and their appearance can change over time (e.g., due to rusting or grease stains). In addition, the working environment may have adverse conditions, such as not having controlled light conditions or reduced physical space [7]. Most current AR systems are not able to robustly detect and estimate the 3D object pose in real time under such conditions. Consequently, AR has not been widely integrated into the production sector, because the tracking ability to align accurate 3D virtual information over the physical objects in real time is a critical factor [1,2]. Secondly, IRT is a developed technology which has widely expanded in recent years. Specifically, IRT is a technology that measures and analyses temperature information using a non-contact device [8]. IRT obtains this information from electromagnetic radiation called infrared radiation, whose wavelength is longer than the visible light spectrum. Although the infrared radiation theoretically covers a range of wavelengths \(\lambda\) from 0.7 um to 1000 um, for practical purposes the ranges used by the infrared sensors covers values of \(\lambda\) from 1 um to 15 um; these ranges being delimited by the atmospheric transmission windows. The physical basis of IRT has been explained in terms of thermal radiation theory [9]. Its specific characteristics of non-contact measurement and harmless radiation make IRT a useful tool in numerous industrial and scientific utilities.