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Mobile Outdoor AR Assistance Systems - Insights from a Practical Application

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Dynamics in Logistics (LDIC 2024)

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With the increasing popularity of Augmented Reality (AR) applications, especially for mobile devices, the technology supports several construction projects. Here, AR helps to communicate planned construction projects, as its visualization increases the immersion and is better understood than common approaches. However, these use cases are mainly outdoors, which pose special requirements. For most, the (geo-referenced) 3D models of planning projects must be aligned correctly in natural environments, which is a challenge, as many AR devices and standard methods are not working for (large) outdoor environments. For this reason, new research approaches based on different algorithms and sensors arise. This paper defines requirements for developing geo-referenced outdoor AR applications by a structured literature analysis and developing an application with the key requirements: accurate 3D model placement and integration. Creating the mobile outdoor AR application further provides insights for developing such systems. The application considers several outdoor activity requirements and addresses different approaches to geo-referencing with internal and external sensors. This paper also presents two model integration methods: a 2D and a 3D environment scan and algorithmic processing.

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  1. Lee, G.A., Billinghurst, M.: A component based framework for mobile outdoor AR applications. In: Wang, C.C.L. (eds.) Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry, pp. 207–210. ACM, New York, NY (2013)

    Google Scholar 

  2. Stern, H., Leder, R., Lütjen, M.: Human-centered development and evaluation of an AR-assistance system to support maintenance and service operations at LNG ship valves. In: Sihn, W., Schlund, S. (eds.) Competence development and learning assistance systems for the data-driven future, pp. 279–302. Goto Verlag, Gito Verlag, Berlin (2021)

    Google Scholar 

  3. Joshi, R., Hiwale, A., Birajdar, S., Gound, R.: Indoor navigation with augmented reality. In: Kumar, A., Mozar, S. (eds.) ICCCE 2019. LNEE, vol. 570, pp. 159–165. Springer, Singapore (2020).

  4. Arth, C., Pirchheim, C., Ventura, J., Schmalstieg, D., Lepetit, V.: Instant Outdoor Localization and SLAM Initialization from 2.5D Maps (2015). (11):1309–1318.

  5. Burkard, S., Fuchs-Kittowski, F.: Mobile outdoor AR application for precise visualization of wind turbines using digital surface models. In: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management. Science and Technology Publications, pp. 15–24 (2022)

    Google Scholar 

  6. Pascoal, R., Almeida, A.D., Sofia, R.C.: Mobile Pervasive Augmented Reality Systems—MPARS: The Role of User Preferences in the Perceived Quality of Experience in Outdoor Applications (2020). 1533–5399 20(1):7:1‐7:17.

  7. Hansen, L.H., Kjems, E.: Augmented reality for infrastructure information: challenges with information flow and interactions in outdoor environments especially on construction sites. In: Architecture in the Age of the 4th Industrial Revolution: Proceedings of the 37th eCAADe and 23rd SIGraDi Conference, pp. 473–482 (2019)

    Google Scholar 

  8. Fenais, A., Ariaratnam, S.T., Smilovsky, N.: Assessing the Accuracy of an Outdoor Augmented Reality Solution for Mapping Underground Utilities (2020). 1949–1204 11(3):04020029–1–04020029–9.

  9. Behzadan, A.H., Kamat, V.R.: Geo-referenced Registration of Construction Graphics in Mobile Outdoor Augmented Reality 21(4), 247–258 (2007). 0887–3801

    Google Scholar 

  10. Rao, J., Qiao, Y., Ren, F., Wang, J., Du, Q.: A mobile outdoor augmented reality method combining deep learning object detection and spatial relationships for geovisualization 17(9), 1951 (2017). 1424–8220.

  11. Azuma, R., Hoff, B., Neely, H., Sarfaty, R.: A motion-stabilized outdoor augmented reality system. In: Proceedings IEEE Virtual Reality (Cat. No. 99CB36316), pp. 252–259. IEEE Comput. Soc (1999)

    Google Scholar 

  12. Ogawa, T., Mashita, T.: Occlusion handling in outdoor augmented reality using a combination of map data and instance segmentation. In: 2021 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 246–250. IEEE (2021)

    Google Scholar 

  13. Azuma, R.T.: A survey of augmented reality. Presence: Teleoperators Virtual Environ. 6(4), 355–385 (1997).

  14. Leder, R., Stern, H., Freitag, M.: Towards design guidance for the digitalisation of work instructions by focusing on technological possibilities and industrial requirements. Procedia CIRP 109, 466–471 (2022).

    Article  Google Scholar 

  15. Dünser, A., Billinghurst, M., Wen, J., Lehtinen, V., Nurminen, A.: Exploring the use of handheld AR for outdoor navigation 36(8), 1084–1095 (2012). 0097–8493.

  16. Kerr, S.J., et al.: Wearable mobile augmented reality: evaluating outdoor user experience. In: Liu, Z.-Q., Yip, C.F.K., Jorge, J., Liu, Z.-Q. (eds.) Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry, VRCAI 2011: proceedings of the ACM SIGGRAPH Conference on Virtual-Reality Continuum and its Applications to Industry, Hong Kong, China, 11–12 December 2011. Association for Computing Machinery; ACM, Place of publication not identified, pp. 209–216 (2011)

    Google Scholar 

  17. Liu, W., et al.: Learning to match 2D images and 3D LiDAR point clouds for outdoor augmented reality. In: 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pp. 654–655. IEEE (2020)

    Google Scholar 

  18. Behzadan, A.H., Timm, B.W., Kamat, V.R.: General-purpose modular hardware and software framework for mobile outdoor augmented reality applications in engineering 22(1), 90–105 (2007).

  19. Rahul Prabala (2017) Small-form Spatially Augmented Reality on the Jetson TX1. Zugegriffen: 17. April 2023

  20. Reitmayr, G., Drummond, T.: Going out: robust model-based tracking for outdoor augmented reality. In: 2006 IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 109–118. IEEE (2006)

    Google Scholar 

  21. Burkard, S., Fuchs-Kittowski, F.: User-aided global registration method using geospatial 3D data for large-scale mobile outdoor augmented reality. In: 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 104–109 (2020)

    Google Scholar 

  22. Polvi, J., Taketomi, T., Yamamoto, G., Dey, A., Sandor, C., Kato, H.: SlidAR: a 3D positioning method for SLAM-based handheld augmented reality. 55, 33–43 (2016). 0097–8493.

  23. Wei, H., Liu, Y., Xing, G., Zhang, Y., Huang, W.: Simulating shadow interactions for outdoor augmented reality with RGBD data. 7, 75292–75304 (2019). 2169-3536.

  24. Karami, E., Shehata, M., Smith, A.: Image Identification Using SIFT Algorithm: Performance Analysis against Different Image Deformations (2017). arXiv

    Google Scholar 

  25. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. 60(6), 84–90 (2017). 0001–0782.

  26. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE (2014)

    Google Scholar 

  27. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. LNCS, vol 9351, pp. 234–241. Springer, Cham (2015).

  28. Charles, R.Q., Su, H., Kaichun, M., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77–85. IEEE (2017)

    Google Scholar 

  29. Zhang, R., Li, G., Li, M., Wang, L.: Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning. ISPRS J. Photogram. Remote Sens. 85–96 (2018).

  30. Tran, T.T.M., Brown, S., Weidlich, O., Billinghurst, M., Parker, C.: Wearable Augmented Reality: Research Trends and Future Directions from Three Major Venues, pp. 1941–0506 (2023). 29(11):4782–4793.

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This contribution was part of the “RailAR – Assistenzsystem zur optimierten Lärmschutzplanung und AR-basierten Darstellung eines Planungsstandes von Eisenbahntrassen“ project. This project was supported by the Federal Ministry for Economic Affairs and Climate Action (BMWK) on the basis of a decision by the German Bundestag (funding number 16KN062839).

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Correspondence to Rieke Leder .

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Leder, R., Zeitler, W., Stern, H., Lütjen, M., Freitag, M. (2024). Mobile Outdoor AR Assistance Systems - Insights from a Practical Application. In: Freitag, M., Kinra, A., Kotzab, H., Megow, N. (eds) Dynamics in Logistics. LDIC 2024. Lecture Notes in Logistics. Springer, Cham.

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