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2023 | Vol. 2(29) | 69--79
Tytuł artykułu

Universal structural map for indoor navigation in university campus

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EN
Abstrakty
EN
Mapmodeling is an important aspect of indoor navigation. It involves creating a digital map of the indoor environment that can be used for navigation purposes. The map can be created using various techniques such as laser scanning, photogrammetry, and computer vision. Once the map is created, it can be used to develop navigation algorithms that can help users navigate the indoor environment. In this paper, we propose a solution based on CAD files. These models can be used for a variety of purposes, including indoor navigation. There are many CAD applications available, including AutoCAD, SketchUp, and SolidWorks, among others. It is a relatively cheap method to model any indoor environment from scans of plans or CAD files. CAD files are the most accurate way to build a digital indoor map because these files can include 2D or 3D designs and usually contain important location information (e.g. floor level) within the layer properties. Moreover, we can map the CAD annotation to the following feature classes if they conform to the indoors model: types of rooms, types of doors, etc. We propose a solution based on DXF format files. We developed the parser to transfer the necessary data from the CAD files to the navigation system. It consists of processing existing maps from the CAD format to the appropriate structure, supplementing it with the data of BLE transmitters, and saving it as a graph suitable for determining routes and guiding the user along the route. We use our method in a system that supports navigation and user safety, emphasizing users with special needs, which we are implementing on our academic campus.
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69--79
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
  • University of Siedlce, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland, piotr.switalsk@uws.edu.pl
  • University of Siedlce, Faculty of Exact and Natural Sciences, Institute of Computer Science, ul. 3 Maja 54, 08-110 Siedlce, Poland, andrzej.salamonczyk@uws.edu.pl
Bibliografia
  • 1. Alnafessah, A., et al.: Developing an ultra wideband indoor navigation system for visually impaired people. International Journal of Distributed Sensor Networks, 12:6152342–6152342, (2016).
  • 2. Daum, S., Borrmann, A.: Processing of Topological BIM Queries using Boundary Representation Based Methods. Advanced Engineering Informatics, Volume28, Issue 4,272-286,ISSN1474-0346, (2014) https://doi.org/10.1016/j.aei.2014.06.001.78
  • 3. Dorninger, P., and Nothegger, C.: 3D Segmentation of Unstructured Point Clouds for Building Modelling. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pages 191–196 (2007).
  • 4. El-Sheimy, N., Youssef, A.: Inertial sensors technologies for navigation applications: State of the art and future trends. Satellite Navigation, 1(1), 2, (2020).
  • 5. El-Sheimy, N. and Li, Y.: Indoor navigation: state of the art and future trends. Satellite Navigation 2, (2021). https://doi.org/10.1186/s43020-021-00041-3
  • 6. Fernando, Nimalika and Mcmeekin, David and Murray, Iain. Modelling indoor spaces to support vision impaired navigation using an ontology based approach. (2019).
  • 7. Gotlib, D., Wyszomirski, M. and Gnat, M.: A Simplified Method of Cartographic Visualisation of Buildings’ Interiors (2D+) for Navigation Applications, ISPRS Int. J. Geo-Inf. 2020, 9(6), 407; https://doi.org/10.3390/ijgi9060407
  • 8. Hu Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A.: Randlanet: Efficient semantic segmentation of large-scale point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11108–11117 (2020).
  • 9. Huang, H. et al.: Location based services: ongoing evolution and research agenda. Journal of Location Based Services. 12:63-93, Taylor & Francis, (2018).
  • 10. Joao,C.Ferreira, R., and Martinho S,: Beacons and BIM Models for Indoor Guidance and Location. Sensors 2018, 18(12), 4374; https://doi.org/10.3390/s18124374
  • 11. Jokar, A., et al.: An Introduction to Open Street Map in Geographic Information Science: Experiences, Research, and Applications, pages 1–15. (2015).
  • 12. Konarski, M., and Zabierowski, W.: Using Google Maps API along with technology .NET. (2010).
  • 13. Kumar, G., et al.: A LiDAR and IMU Integrated Indoor Navigation System for UAVs and Its Application in Real-Time Pipeline Classification. Sensors (Basel, Switzerland), 17, June 2017.
  • 14. Li, H., Yang, X., Zhai, H., Liu, Y., Bao, H.,and Zhang G.: Vox-Surf: Voxel-Based Implicit Surface Representation. In IEEE Transactions on Visualization and Computer Graphics (2022), doi: 10.1109/TVCG.2022.3225844.
  • 15. Li, F., et al.: A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing- UbiComp ’12, page 421, Pittsburgh, Pennsylvania, 2012. ACM Press.
  • 16. Liu, J., Luo, J., Hou, J., Wen, D., Feng, G., and Zhang X.: A BIM Based Hybrid 3D Indoor Map Model for Indoor Positioning and Navigation, ISPRS Int. J. Geo-Inf. 2020, 9(12), 747; https://doi.org/10.3390/ijgi9120747
  • 17. Makochon, K., De Donatis, M.: Outdoor GPS and Indoor Magnetic Field Positioning: Combining survey technology and app development. Rendiconti Online della Società Geologica Italiana , 42, 94–96, (2017).
  • 18. Mikułowski,Dariusz,Pilski, Marek and Terlikowski, Grzegorz. An Approach for Discovering Space by the Blind using an Ontology-based Map and Data from Existing Open Maps. New Frontiers in Communication and Intelligent Systems,207-214 (2021).
  • 19. Ming, H., Yanzhu, D., Jianguang, Z., Yong, Z.: A topological enabled three-dimensional model based on constructive solid geometry and boundary representation. Cluster Comput 19, 2027–2037 (2016). https://doi.org/10.1007/s10586-016-0634-1
  • 20. Morbidoni, C., Pierdicca, R., Quattrini, R., and Frontoni, E.: GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT
  • CLOUDS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W1-2020, 95–102, https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020, (2020).
  • 21. Pešić, S., Radovanović, M. Ivanović, M., Tošić, M., Iković, O., Bošković, B.: Graph-based metadata modeling in indoor positioning systems. Simulation Modelling Practice and Theory Volume 105, 102140, (2020).
  • 22. Skarżyński.K.,Bartyna,W.,Stępniak,M.,Human safety in an ontology-based IoT system Series: Systems and Information Technology. Part 1. Theory and Applicationof Artificial Intelligence Methods,Jerzy Tchórzewski, Piotr Świtalski[editors], Siedlce(2021).
  • 23. Stroud, I.: Boundary Representation Modelling Techniques.Springer-Verlag, Berlin, Heidelberg (2026).
  • 24. Qi,C., Su,H., Mo,K., and Guibas,L.:Point Net: Deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition, pages652–660(2017).
  • 25. Tang,H., and Zhu, Z.:Asegmentation-based stereovision approach for assisting visually impaired people. In International Conference on Computers for Handicapped Persons, pages 581–587. Springer,(2012)
  • 26.Woodman,O., and Harle,R.: Pedestrian localisation for indoor environments. UbiComp ’08: Proceedings of the 10th international conference on Ubiquitous computing. Pages114–123,(2008). https://doi.org/10.1145/1409635.1409651
  • 27. Xu,Y.,Tong,X.,and Stilla,U.:Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry. Autom.Constr., vol.126,p.103675, 2021.
  • 28. Autodesk.Autocad.https://www.autodesk.com/Lastaccessed31July2023
  • 29. Autodesk. Dxf reference. https://images.autodesk.com/adsk/files/autocad_2012_pdf_dxfreference_enu.pdfLastaccessed31July2023
  • 30. https://cdn.neuvition.com/media/blog/lidar-price.htmlLastaccessed31July2023
  • 31. https://flyguys.com/how-much-do-drone-lidar-services-cost/Lastaccessed31July2023
  • 32. Neo4j.Neo4jGraphDatabase.https://neo4j.com/Lastaccessed31July202
Typ dokumentu
Bibliografia
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