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Activity Localization and Tracking in Order Picking Processes using LiDAR Sensors: LiDAR based Activity Tracking

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Published:06 March 2021Publication History

ABSTRACT

Various sensor technologies can be used for the purpose of indoor localization and activity analysis in industrial environments. Quite often such technologies have relevant drawbacks in human-centered applications. This paper describes the novel approach of a LiDAR-based system solution for localization and identification of manual processes in order picking. The solution based on novel LiDAR sensors and referring data processing offers high accuracy and low impact on the user. The results of an initial test application are compared to previous experiences with an ultrasound-based system.

References

  1. A. Syska, „Produktionsmanagement: Das A - Z wichtiger Methoden und Konzepte für die Produktion von heute“, Gabler, 2006, p. 99.Google ScholarGoogle Scholar
  2. R. Koether, „Distributionslogistik – Effiziente Absicherung der Lieferfähigkeit“, Springer Gabler, 2018, p. 134.Google ScholarGoogle ScholarCross RefCross Ref
  3. G. Retscher, M. Kistenich, „Vergleich von Systemen zur Positionsbestimmung und Navigation in Gebäuden“, in zfv – Zeitschrift für Geodäsie, Geoinformation und Landmanagement, Heft 1/2006, Wißner-Verlag, pp. 25-35.Google ScholarGoogle Scholar
  4. R. Mautz, „Indoor positioning technologies“, Habilitation, ETH Zürich Research Collection, 2012, pp. 9-10.Google ScholarGoogle Scholar
  5. F. Zafari, A. Gkelias, K. K. Leung, „A survey of indoor localization systems and technologies“, in IEEE Communications Surveys & Tutorials, Vol. 21, No. 3., 2019, pp. 2568-2599.Google ScholarGoogle Scholar
  6. A.-K. Ahrens, „Bewegungsanalyse im Supermarkt zur Verbesserung von Kommissionierprozessen“, Master Thesis, Otto-von-Guericke University Magdeburg, 2019, pp 45-48.Google ScholarGoogle Scholar
  7. S. Giancola, M. Valenti, R. Sala, „A survey on 3D cameras: metrological comparison of time-of-flight, structured-light and active stereoscopy technologies“, Springer, 2018, p. 21.Google ScholarGoogle ScholarCross RefCross Ref
  8. A. Ortiz Arteaga, D. Scott, J. Boehm, „Initial investigation of a low-cost automotive LiDAR system“, in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W17, 2019, pp. 233-240.Google ScholarGoogle Scholar
  9. Livox Technology, „MID-40 lidar sensor – Livox“, URL: https://www.livoxtech.com/mid-40-and-mid100 (accessed 25.02.2020).Google ScholarGoogle Scholar
  10. R. Muñoz-Salinas, M. J. Marin-Jimenez, E. Yeguas-Bolivar, R. Medina-Carnicer, „Mapping and localization from planar markers“, Pattern Recognition, 73, 2017, pp. 158–171.Google ScholarGoogle ScholarCross RefCross Ref
  11. S. S. Garrido-Jurado, R. Muñoz-Salinas, F. Madrid-Cuevas, R. Medina-Carnicer, „Generation of fiducial marker dictionaries using mixed integer linear programming“, Pattern Recognition, 51, 2015, pp. 481-491.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. D. Meagher, „Geometric modeling using octree encoding“, Computer Graphics and Image Processing, June 1981, pp. 129-147.Google ScholarGoogle Scholar
  13. C.-H. Chen, D. Ramanan, „3D human pose estimation = 2D pose estimation + matching“, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 7035-7043.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Other conferences
    SSIP '20: Proceedings of the 2020 3rd International Conference on Sensors, Signal and Image Processing
    October 2020
    95 pages
    ISBN:9781450388283
    DOI:10.1145/3441233

    Copyright © 2020 ACM

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    Publication History

    • Published: 6 March 2021

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