Skip to main content

3D Navigation for a Mobile Robot

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 694))

Abstract

We propose a novel 3D navigation system for autonomous vehicle path-planning. The system processes a point-cloud data from an RGB-D camera and creates a 3D occupancy grid with adaptable cell size. Occupied grid cells contain normal distribution characterizing the data measured in the area of the cell. The normal distributions are then used for cell classification, traversability, and collision checking. The space of traversable cells is used for path-planning. The ability to work in three-dimensional space allows autonomous robots to operate in highly structured environments with multiple levels, uneven surfaces, and various elevated and underground crossings. That is important for the usage of robots in real-world scenarios such as in urban areas and for disaster rescue missions.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
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

Learn about institutional subscriptions

References

  1. Doroodgar, B., Yugang, L., Goldie, N.: A learning-based semi-autonomous controller for robotic exploration of unknown disaster scenes while searching for victims. IEEE Trans. Cybern. 44, 12 (2014)

    Google Scholar 

  2. Hornung, A., Phillips, M., Jones, E.G., Bennewitz, M., Likhachev, M., Chitta, S.: Navigation in three-dimensional cluttered environments for mobile manipulation. In: IEEE International Conference on Robotics and Automation (ICRA) (2012)

    Google Scholar 

  3. Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W. OctoMap : an efficient probabilistic 3D mapping framework based on octrees. In: IEEE International Conference on Autonomous Robots (2013)

    Google Scholar 

  4. Labbe, M., Michaud, F.: Online global loop closure detection for large-scale multi-session graph-based SLAM. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2014)

    Google Scholar 

  5. Maier, D., Hornung, A., Bennewitz, M.: Real-time navigation in 3D environments based on depth camera data. In: IEEE/RAS International Conference on Humanoid Robots (2012)

    Google Scholar 

  6. Morisset, B., et al.: Leaving flatland: toward real-time 3D navigation. In: IEEE International Conference on Robotics and Automation (ICRA) (2009)

    Google Scholar 

  7. Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K.: The office marathon: robust navigation in an indoor office environment. In: IEEE International Conference on Robotics and Automation (ICRA) (2010)

    Google Scholar 

  8. Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: IEEE International Conference on Robotics and Automation (ICRA) (2011)

    Google Scholar 

  9. Stoyanov, T., Magnusson, M., Almqvist, H., Lilienthal, A.: On the accuracy of the 3D normal distributions transform as a tool for spatial representation. In: IEEE International Conference on Intelligent Robots and Systems (IROS) (2011)

    Google Scholar 

  10. Stoyanov, T., Magnusson, M., Andreasson, H., Lilienthal, A.: Path planning in 3D environments using the normal distributions transform. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010)

    Google Scholar 

  11. Škoda, J.: 3D Navigation for Mobile Robots, Master thesis, Charles University (2017)

    Google Scholar 

  12. Xu, S., Honegger, D., Pollefeys, M., Heng, L.: Real-time 3D navigation for autonomous vision-guided MAVs. In: IEEE International Conference on Intelligent Robots and Systems (IROS) (2015)

    Google Scholar 

Download references

Acknowledgements

We would like to thank Dr. Ohya from University of Tsukuba, Japan, for his valuable insight that helped with this work and Dr. Obdžálek for help with the robotic platform. Research is supported by the Czech Science Foundation under the contract P103-15-19877S.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Škoda .

Editor information

Editors and Affiliations

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

Škoda, J., Barták, R. (2018). 3D Navigation for a Mobile Robot. 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 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70836-2_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70835-5

  • Online ISBN: 978-3-319-70836-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics