## 5 Conclusions  
MANTRA is a novel AR system that successfully combines IRT and AR technologies for use in the industrial maintenance sector. This system was both tested quantitatively and qualitatively validated, showing excellent results due the new combination of methods for detection and pose estimation. Specifically, MANTRA can automatically align virtual information and temperature on any 3D object, in real time. MANTRA was designed as a modular system, so the Hardware and Software components can be replaced or upgraded (e.g., using another RGB-D camera or object detector method). It was also demonstrated the benefits of adding the IRT technology to an AR system to increase the effectiveness in maintenance operations. In addition, the IRT - RGB-D registration procedure explained with our new calibration pattern would be very helpful for popularizing the IRT and AR combination. Moreover, MANTRA has a lot of potential in other sectors, such as manufacturing or quality inspection.  
As a future work, we plan to replace the RGB-D camera by a RGB camera and replace the visualization display with a head-mounted display, such as the Hololens 2. Another area for improvement is to replace LINEMOD method with future object detection and pose estimation method based on deep-learning that would surpass it. It is also planned to perform a field-scale experiment with another industrial maintenance use-case such as water pump repair operations.  
Figure 14: Questionnaires results carried out after the maintenance task: (a) SUS results; and (b) NASA results.  
Figure 15: NASA Results.  
**Author Contributions:** Conceptualization, M.O. and E.I; methodology, M.O. and E.I.; software, M.O. and E.I.; validation, P.V., A.J., and M.O.; formal analysis, M.O. and E.I.; investigation, M.O., E.I., and P.V.; resources, M.A.; data curation, M.O. and E.I.; writing--original draft preparation, M.O., E.I., and A.J.; writing--review and editing, M.O., E.I., and P.V.; visualization, M.A.; supervision, J.M.; project administration, J.M.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The MANTRA project was funded by the Spanish Ministry of Economy and Competitiveness, through its program Retos-Colaboracion with grant number RTC-2017-6312-7. In addition, GPU servers for deep-learning funded by the European Union through the ERDF (European Regional Development Fund) program of the Valencian Community 2014-2020 (IDIFEDER/2018/029) were employed for training some of the algorithms applied in this work.  
**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Universitat Politecnica de Valencia (P2 _11 _01_ 18, 11 of January 2018).  
**Informed Consent Statement:** Written informed consent has been obtained from the user subjects to publish this paper.  
**Data Availability Statement:** LINEMOD and LINEMOD-OCCLUDED dataset can be obtained at [[https://bop.felk.cvut.cz/datasets/](https://bop.felk.cvut.cz/datasets/)]([https://bop.felk.cvut.cz/datasets/](https://bop.felk.cvut.cz/datasets/)).  
**Conflicts of Interest:** The authors declare no conflict of interest.