Skip to main content
SpringerLink
Log in

LLN-SLAM: A Lightweight Learning Network Semantic SLAM

  • Conference paper
  • First Online:
Intelligence Science and Big Data Engineering. Big Data and Machine Learning (IScIDE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11936))

  • 1737 Accesses

Abstract

Semantic SLAM is a hot research subject in the field of computer vision in recent years. The mainstream semantic SLAM method can perform real-time semantic extraction. However, under resource-constrained platforms, the algorithm does not work properly. This paper proposes a lightweight semantic LLN-SLAM method for portable devices. The method extracts the semantic information through the matching of the Object detection and the point cloud segmentation projection. In order to ensure the running speed of the program, lightweight network MobileNet is used in the Object detection and Euclidean distance clustering is applied in the point cloud segmentation. In a typical augmented reality application scenario, there is no rule to avoid the movement of others outside the user in the scene. This brings a big error to the visual positioning. So, semantic information is used to assist the positioning. The algorithm does not extract features on dynamic semantic objects. The experimental results show that the method can run stably on portable devices. And the positioning error caused by the movement of the dynamic object can be effectively corrected while establishing the environmental semantic map.

This work was realized by a student. This work is supported by National Key R&D Program of China (2018YFB1004904) and Nation Key Technology Research and Development of china during the “13th Five Year Plan”: 41401010203, 315050502, 31511040202.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Salas-Moreno, R.F.: Dense semantic SLAM. Doctoral dissertation, Imperial College London (2014)

    Google Scholar 

  2. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  3. 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 

  4. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  5. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

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

    Google Scholar 

  7. Yu, J.S., Wu, H., Tian, G.H., et al.: Semantic database design and semantic map construction of robots based on the cloud. Robot 38(4), 410–419 (2016)

    Google Scholar 

  8. Li, X., Ao, H., Belaroussi, R., Gruyer, D.: Fast semi-dense 3D semantic mapping with monocular visual SLAM. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 385–390. IEEE (2017)

    Google Scholar 

  9. McCormac, J., Handa, A., Davison, A., Leutenegger, S.: Semanticfusion: dense 3D semantic mapping with convolutional neural networks. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4628–4635. IEEE (2017)

    Google Scholar 

  10. Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1052–1067 (2007)

    Article  Google Scholar 

  11. Liu, W., et al.: 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 

  12. Howard, A.G., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  13. Trevor, A.J., Gedikli, S., Rusu, R.B., Christensen, H.I.: Efficient organized point cloud segmentation with connected components. In: Semantic Perception Mapping and Exploration (SPME) (2013)

    Google Scholar 

  14. Nowozin, S.: Optimal decisions from probabilistic models: the intersection-over-union case. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 548–555 (2014)

    Google Scholar 

  15. Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3-D mapping with an RGB-D camera. IEEE Trans. Robot. 30(1), 177–187 (2014)

    Article  Google Scholar 

  16. Bowman, S.L., Atanasov, N., Daniilidis, K., Pappas, G.J.: Probabilistic data association for semantic SLAM. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1722–1729. IEEE (2017)

    Google Scholar 

  17. Ma, L., Stückler, J., Kerl, C., Cremers, D.: Multi-view deep learning for consistent semantic mapping with RGB-D cameras. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 598–605. IEEE (2017)

    Google Scholar 

  18. DeTone, D., Malisiewicz, T., Rabinovich, A.: Toward geometric deep SLAM. arXiv preprint arXiv:1707.07410 (2017)

  19. Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: Unsupervised learning of depth and ego-motion from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1851–1858 (2017)

    Google Scholar 

  20. Kendall, A., Grimes, M., Cipolla, R.: Posenet: a convolutional network for real-time 6-DOF camera relocalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2938–2946 (2015)

    Google Scholar 

  21. Yang, N., Wang, R., Stuckler, J., Cremers, D.: Deep virtual stereo odometry: leveraging deep depth prediction for monocular direct sparse odometry. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 835–852. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-030-01237-3_50

    Chapter  Google Scholar 

  22. Carvalho, L.E., von Wangenheim, A.: 3D object recognition and classification: a systematic literature review. Pattern Anal. Appl. 1–50 (2019)

    Google Scholar 

  23. Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6D object pose prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 292–301 (2018)

    Google Scholar 

  24. Brachmann, E., Rother, C.: Learning less is more-6D camera localization via 3D surface regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4654–4662 (2018)

    Google Scholar 

  25. Sturm, J., Burgard, W., Cremers, D.: Evaluating egomotion and structure-from-motion approaches using the TUM RGB-D benchmark. In: Proceedings of the Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS) (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Xichao Qu & Weiqing Li

Authors
  1. Xichao Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weiqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiqing Li .

Editor information

Editors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Zhen Cui

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

  3. Nanjing University of Science and Technology, Nanjing, China

    Shanshan Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Liang Xiao

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qu, X., Li, W. (2019). LLN-SLAM: A Lightweight Learning Network Semantic SLAM. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36204-1_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36203-4

  • Online ISBN: 978-3-030-36204-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation