Skip to main content
SpringerLink
Log in

3D Object Mapping Using a Labelling System

  • Conference paper
  • First Online:
ROBOT 2017: Third Iberian Robotics Conference (ROBOT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 693))

Included in the following conference series:

Abstract

3D data has arisen as the most used information for environment representation thanks to the advent of low cost RGB-D cameras. We propose a 3D map representation that uses not only depth information but the information provided by an expert system. This expert consists on a Convolutional Neural Network trained with deep learning techniques for scene labelling purposes. For every partial 3D map captured, we receive a set of labels with their associated probability of presence in that scene. The final map is obtained by registering and merging all these partial maps. The semantic labels from the expert system are used to recognise and locate objects in the environment.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  2. Bhattacharya, P., Gavrilova, M.: Roadmap-based path planning - using the voronoi diagram for a clearance-based shortest path. IEEE Robot. Autom. Mag. 15(2), 58–66 (2008)

    Article  Google Scholar 

  3. Booij, O., Terwijn, B., Zivkovic, Z., Kröse, B.: Navigation using an appearance based topological map. In: International Conference on Robotics and Automation. IEEE (2007)

    Google Scholar 

  4. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: IEEE Conference on CVPR (2009)

    Google Scholar 

  5. Du, S., Zhu, J., Zheng, N., Liu, Y., Li, C.: Robust iterative closest point algorithm for registration of point sets with outliers. Opt. Eng. 50(8), 087001 (2011)

    Article  Google Scholar 

  6. Escalona, F., Rodríguez, A., Gómez-Donoso, F., Martínez-Gómez, J., Cazorla, M.: 3D object detection with deep learning. J. Phys. Agents 8(1), 3–10 (2017)

    Google Scholar 

  7. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of 22nd ACM International Conference on Multimedia (2014)

    Google Scholar 

  8. Lee, H., Grosse, R., Ranganath, R., Ng, A.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)

    Google Scholar 

  9. Martínez-Gómez, J., Morell, V., Cazorla, M., García-Varea, I.: Semantic localization in the PCL library. Robot. Auton. Syst. 75(Part B), 641–648 (2016)

    Article  Google Scholar 

  10. Papazov, C., Burschka, D.: An efficient ransac for 3d object recognition in noisy and occluded scenes. In: Computer Vision-ACCV 2010, pp. 135–148 (2011)

    Google Scholar 

  11. PointClouds: The PCL Registration API (2014). http://pointclouds.org/documentation/tutorials/registration_api.php. Accessed 30 Apr 2017

  12. Pronobis, A., Mozos, O.M., Caputo, B., Jensfelt, P.: Multi-modal semantic place classification. Int. J. Robot. Res. 29, 298–320 (2009)

    Article  Google Scholar 

  13. Rodriguez, A., Gomez-Donoso, F., Martinez-Gomez, J., Cazorla, M.: Building 3D maps with tag information. In: XVII Workshop en Agentes Físicos (2016)

    Google Scholar 

  14. ROS: Pepper Tutorials (2014). http://wiki.ros.org/pepper/Tutorials

  15. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA 2009 (2009)

    Google Scholar 

  16. Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of 15th ACM International Conference on Multimedia (2007)

    Google Scholar 

  17. Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Ninth IEEE International Conference on Computer Vision (2003)

    Google Scholar 

  18. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of IEEE Conference on CVPR (2015)

    Google Scholar 

  19. Thrun, S., et al.: Robotic mapping: a survey. In: Exploring Artificial Intelligence in the New Millennium, pp. 1–35 (2002)

    Google Scholar 

  20. Tombari, F.: Keypoints and features. In: cGLIBS, Pisa (2013). http://www.pointclouds.org/assets/uploads/cglibs13_features.pdf

Download references

Acknowledgment

This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds, and by grant of Vicerrectorado de Investigación y Transferencia de Conocimiento para el fomento de la I+D+i en la Universidad de Alicante 2016.

Author information

Authors and Affiliations

  1. Computer Science Research Institute, University of Alicante, P.O. Box 99, 03080, Alicante, Spain

    Félix Escalona, Francisco Gomez-Donoso & Miguel Cazorla

Authors
  1. Félix Escalona
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Gomez-Donoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel Cazorla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Félix Escalona .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Sevilla, Spain

    Anibal Ollero

  2. Institut de Robòtica I Informàtica Industrial (CSIC-UPC), Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Sanfeliu

  3. Departamento de Informática e Ingeniería de Sistemas, Escuela de Ingeniería y Arquitectura, Instituto de Investigación en Ingeniería de Aragón, Zaragoza, Spain

    Luis Montano

  4. Institute of Electronics and Telematics Engineering of Aveiro (IEETA), Universidade de Aveiro, Aveiro, Portugal

    Nuno Lau

  5. IDMEC, Instituto Superior Técnico de Lisboa, Universidade de Lisboa, Lisbon, Portugal

    Carlos Cardeira

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Escalona, F., Gomez-Donoso, F., Cazorla, M. (2018). 3D Object Mapping Using a Labelling System. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-70833-1_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70833-1_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70832-4

  • Online ISBN: 978-3-319-70833-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation