Skip to main content
Springer Nature Link
Log in

Object Detection for Indoor Localization System

  • Conference paper
  • First Online:
Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

  • 1007 Accesses

Abstract

The urge for robust and reliable localization systems for autonomous mobile robots (AMR) is increasing since the demand for these automated systems is rising in service, industry, and other areas of the economy. The localization of AMRs is one of the crucial challenges, and several approaches exist to solve this. The most well-known localization systems are based on LiDAR data due to their reliability, accuracy, and robustness. One standard method is to match the reference map information with the actual readings from LiDAR or camera sensors, allowing localization to be performed. However, this approach has difficulties handling anything that does not belong to the original map since it affects the matching algorithm’s performance. Therefore, they should be considered outliers. In this paper, a deep learning-based object detection algorithm is not only used for detection but also to classify them as outliers from the localization’s perspective. This is an innovative approach to improve the localization results in a real mobile platform. Results are encouraging, and the proposed methodology is being tested in a real robot.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Siegwart, R., Nourbakhsh, I.R., Scaramuzza, D.: Introduction to Autonomous Mobile Robots. MIT Press, Cambridge (2011)

    Google Scholar 

  2. Lauer, M., Lange, S., Riedmiller, M.: Calculating the perfect match: an efficient and accurate approach for robot self-localization. In: Bredenfeld, A., Jacoff, A., Noda, I., Takahashi, Y. (eds.) RoboCup 2005. LNCS (LNAI), vol. 4020, pp. 142–153. Springer, Heidelberg (2006). https://doi.org/10.1007/11780519_13

    Chapter  Google Scholar 

  3. Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611, pp. 586–606. Spie (1992)

    Google Scholar 

  4. Biber, P., Straßer, W.: The normal distributions transform: a new approach to laser scan matching. In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003)(Cat. No. 03CH37453), vol. 3, pp. 2743–2748. IEEE (2003)

    Google Scholar 

  5. Zhao, Z.-Q., Zheng, P., Shou-tao, X., Xindong, W.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)

    Article  Google Scholar 

  6. Xiao, Y., et al.: A review of object detection based on deep learning. Multimedia Tools Appl., 23729–23791 (2020). https://doi.org/10.1007/s11042-020-08976-6

  7. Zhiqiang, W., Jun, L.: A review of object detection based on convolutional neural network. In: 2017 36th Chinese Control Conference (CCC), pp. 11104–11109. IEEE (2017)

    Google Scholar 

  8. Shabbir, J., Anwer, T.: A survey of deep learning techniques for mobile robot applications. arXiv preprint arXiv:1803.07608 (2018)

  9. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I–I (2001)

    Google Scholar 

  10. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE (2005)

    Google Scholar 

  11. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  12. Rajaratnam, S.: Development of a real-time vision framework for autonomous mobile robots in human-centered environments. Master’s thesis, University of Toronto, Canada (2019)

    Google Scholar 

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

    Google Scholar 

  14. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  15. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28, pp. 91–99 (2015)

    Google Scholar 

  16. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  17. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  18. Cai, Z., Vasconcelos, N.: Cascade R-CNN: delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018)

    Google Scholar 

  19. Cai, L., et al.: MaxpoolNMS: getting rid of NMS bottlenecks in two-stage object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9356–9364 (2019)

    Google Scholar 

  20. Pramanik, A., Pal, S.K., Maiti, J., Mitra, P.: Granulated RCNN and multi-class deep sort for multi-object detection and tracking. IEEE Trans. Emerg. Topics Comput. Intell. 6, 171–181(2021)

    Google Scholar 

  21. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229 (2013)

  22. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  23. Liu, W.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  24. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  25. Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018

    Google Scholar 

  26. Zhao, Q., et al.: M2Det: a single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9259–9266 (2019)

    Google Scholar 

  27. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  28. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  29. Wang, C.-Y., Yeh, I.-H., Liao, H.-Y.M.: You only learn one representation: unified network for multiple tasks. arXiv preprint arXiv:2105.04206 (2021)

  30. Anati, R., Scaramuzza, D., Derpanis, K.G., Daniilidis, K.: Robot localization using soft object detection. In: 2012 IEEE International Conference on Robotics and Automation, pp. 4992–4999. IEEE (2012)

    Google Scholar 

  31. Dourado, C.M.J.M., et al.: A new approach for mobile robot localization based on an online IoT system. Future Gener. Comput. Syst. 100, 859–881 (2019). Already included

    Google Scholar 

  32. Ekvall, S., Kragic, D., Jensfelt, P.: Object detection and mapping for service robot tasks. Robotica 25(2), 175–187 (2007)

    Article  Google Scholar 

  33. Astua, C., Barber, R., Crespo, J., Jardon, A.: Object detection techniques applied on mobile robot semantic navigation. Sensors 14, 6734–6757 (2014)

    Google Scholar 

  34. Espinace, P., Kollar, T., Roy, N., Soto, A.: Indoor scene recognition by a mobile robot through adaptive object detection. Robot. Auton. Syst. 61, 932–947 (2013)

    Google Scholar 

  35. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)

    Google Scholar 

  36. Asad, M.H., Khaliq, S., Yousaf, M.H., Ullah, M.O., Ahmad, A.: A real-time and AI-on-the-edge perspective. In: Advances in Civil Engineering, Pothole Detection Using Deep Learning (2022)

    Google Scholar 

  37. Nepal, U., Eslamiat, H.: Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs. Sensors 22(2), 464 (2022)

    Article  Google Scholar 

  38. Jiang, Z., Zhao, L., Li, S., Jia, Y.: Real-time object detection method for embedded devices. In: Computer Vision and Pattern Recognition (2020)

    Google Scholar 

  39. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  40. Génération Robots: Hokuyo URG-04-LX-UG01 laser range finder. Accessed 30 Aug 2022

    Google Scholar 

Download references

Acknowledgment

The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI20/11780028.

Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    João Braun, João Mendes, Ana I. Pereira & José Lima

  2. INESC TEC - INESC Technology and Science, Faculty of Engineering of University of Porto, Porto, Portugal

    João Braun, José Lima & Paulo Costa

  3. Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    João Braun, João Mendes & José Lima

Authors
  1. João Braun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João Mendes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ana I. Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Paulo Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to João Braun .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Braun, J., Mendes, J., Pereira, A.I., Lima, J., Costa, P. (2022). Object Detection for Indoor Localization System. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23236-7_54

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics