Skip to main content
SpringerLink
Log in

Mobile Outdoor AR Assistance Systems - Insights from a Practical Application

  • Conference paper
  • First Online:
Dynamics in Logistics (LDIC 2024)

Part of the book series: Lecture Notes in Logistics ((LNLO))

Included in the following conference series:

  • 123 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  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). https://doi.org/10.1007/978-981-13-8715-9_20

  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. https://doi.org/10.1109/TVCG.2015.2459772

  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. https://doi.org/10.1145/3375458

  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. https://doi.org/10.1061/(ASCE)PS.1949-1204.0000474

  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. https://doi.org/10.3390/s17091951

  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). https://doi.org/10.1162/pres.1997.6.4.355

  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). https://doi.org/10.1016/j.procir.2022.05.279

    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. https://doi.org/10.1016/j.cag.2012.10.001

  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). https://doi.org/10.1016/j.aei.2007.08.005

  19. Rahul Prabala (2017) Small-form Spatially Augmented Reality on the Jetson TX1. http://stanford.edu/class/ee367/Winter2017/prabala_ee367_win17_report.pdf. 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. https://doi.org/10.1016/j.cag.2015.10.013

  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. https://doi.org/10.1109/ACCESS.2019.2920950

  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. https://doi.org/10.1145/3065386

  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). https://doi.org/10.1007/978-3-319-24574-4_28

  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). https://doi.org/10.1016/j.isprsjprs.2018.04.022

  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. https://doi.org/10.1109/TVCG.2023.3320231

Download references

Acknowledgment

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).

Author information

Authors and Affiliations

  1. BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Hochschulring 20, 28359, Bremen, Germany

    Rieke Leder, Waldemar Zeitler, Michael Lütjen & Michael Freitag

  2. Faculty of Production Engineering, University of Bremen, Badgasteiner Straße 1, 28359, Bremen, Germany

    Hendrik Stern & Michael Freitag

Authors
  1. Rieke Leder
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Waldemar Zeitler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hendrik Stern
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Lütjen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Freitag
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rieke Leder .

Editor information

Editors and Affiliations

  1. Production Systems and Logistics, University of Bremen, Bremen, Germany

    Michael Freitag

  2. Global Supply Chain Management, University of Bremen, Bremen, Germany

    Aseem Kinra

  3. Crowley Center for Transportation and Logistics, University of North Florida, Jacksonville, FL, USA

    Herbert Kotzab

  4. Combinatorial Optimization and Logistics, University of Bremen, Bremen, Germany

    Nicole Megow

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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. https://doi.org/10.1007/978-3-031-56826-8_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56826-8_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56825-1

  • Online ISBN: 978-3-031-56826-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation