Skip to main content
SpringerLink
Log in

Air-ground cooperative topometric mapping of traversable ground

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

In this paper, we propose an approach for cooperative mapping of traversable ground from aerial and ground views in structured outdoor and indoor environments. The presented approach achieves a hybrid map building based on traversable ground skeletonization and graph matching. The obtained map is an augmented ground traversability map, represented as a hybrid topological/metric graph from heterogeneous sources. This approach provides a very suitable representation for ground navigation and planning. To validate this approach, the proposed algorithm is applied between aerial views, provided by a UAV flying over an experimental site, and ground maps from ground robots at different exploration stages, in realistic simulation and real-world environments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

Notes

  1. Situated in Clermont-Ferrand town in France.

  2. Using ROS Indigo and Ubuntu(64-bit) 14.4. Computer specification: Intel Core i7 @2.5GHz, 16GB RAM.

References

  • Almasi, S., Lauric, A., Malek, A., & Miller, E. L. (2018). Cerebrovascular network registration via an efficient attributed graph matching technique. Medical Image Analysis, 46, 118–129.

    Article  Google Scholar 

  • Beeson, P., Jong, N. K.,& Kuipers, B. (2005) Towards autonomous topological place detection using the extended voronoi graph. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 4373–4379, IEEE.

  • Bosch, S., Lacroix, S., & Caballero, F. (2006). Autonomous detection of safe landing areas for an UAV from monocular images. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

  • Boumghar, R., Lacroix, S.,& Lefebvre, O. (2011). An information-driven navigation strategy for autonomous navigation in unknown environments. In IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE.

  • Burkard, R. E. (2013). Quadratic assignment problems (pp. 2741–2814). New York, NY: Springer.

    Google Scholar 

  • Cantelli, L. (2013). UAV/UGV cooperation for surveying operations in humanitarian demining. In IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), IEEE.

  • Demirci, F., Shokoufandeh, A., & Dickinson, S. (2014). Many-to-Many graph matching (pp. 472–477). Boston: Springer.

    Google Scholar 

  • Elfes, A., Bergerman, M., Carvalho, J. R. H., de Paiva, E. C., Ramaos, J. J. G., & Bueno, S. S. (1999). Air-ground robotic ensembles for cooperative applications: Concepts and preliminary results, In 2nd International Conference on Field and service Robotics, Pittsburgh, Pa (USA).

  • Farazmanesh, D., & Tavakoli, A. (2018). Morphological decomposition and compression of binary images via a minimum set cover algorithm. Journal of Mathematical Imaging and Vision, 61, 71–83.

    Article  MathSciNet  Google Scholar 

  • Garzon Oviedo, M., Pereira Valente, J. R., Zapata, D., Chill, R., &  Barrientos Cruz, A. (2012). Towards a ground navigation system based in visual feedback provided by a mini UAV. In IEEE Intelligent Vehicles Symposium Workshops 2012, Car.

  • Garzon, M., Valente, J., Zapata, D., & Barrientos, A. (2013). An aerial-ground robotic system for navigation and obstacle mapping in large outdoor areas. Sensors (Basel, Switzerland), 13, 1247–1267.

    Article  Google Scholar 

  • GraphM package: Approximate graph matching algorithms, Technical Report.

  • Guastella, D. C., Cantelli, L., Melita, C. D.,& Muscato, G. (2017). A global path planning strategy for a UGV from aerial elevation maps for disaster response. In ICAART 2017—9th International Conference on Agents and Artificial Intelligence, pp. 335–342, SCITEPRESS—Science and Technology Publications.

  • Guérin, F., Guinand, F., Brethé, J.-F., Pelvillain, H., et al. (2015). UAV-UGV cooperation for objects transportation in an industrial area. In IEEE International Conference on Industrial Technology (ICIT), pp. 547–552, IEEE.

  • Hackeloeer, A., Klasing, K., Krisp, J.M., & Meng, L. (2013). Comparison of point matching techniques for road network matching. International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XL-2W1.

  • Kuhn, H. W. (1955). The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2, 83–97.

    Article  MathSciNet  Google Scholar 

  • Kuipers, B. (2007) An intellectual history of the spatial semantic hierarchy. In Robotics and cognitive approaches to spatial mapping, pp. 243–264, Springer.

  • Leordeanu, M., & Hebert, M. (2005). A spectral technique for correspondence problems using pairwise constraints, In Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 2, pp. 1482–1489, IEEE.

  • Lisien, B., Morales, D., & Silver, D. (2005). The hierarchical atlas. IEEE Transactions on Robotics, 21(3), 473–481.

    Article  Google Scholar 

  • Luan, X. C. (2012). A structure-based approach for matching road junctions with different coordinate systems. In Proceedings of the Twenty-second ISPRS Congress.

  • Mademlis, A., & Strintzis, M. G. (2010) A map-search framework based on attributed graph matching. IEEE Computer Society, 17, 24–33.

  • Mahendran, A., Dewan, A., Soni, N., & Krishna, K. M. (2013). UGV-MAV collaboration for augmented 2d maps. In AIR ’13 Proceedings of Conference on Advances In Robotics, pp. 1–6, ACM Press.

  • Marie, R., Labbani-Igbida, O., & Mouaddib, E. M. (2016). The delta medial axis: A fast and robust algorithm for filtered skeleton extraction. Pattern Recognition, 56, 26–39.

    Article  Google Scholar 

  • Merveilleux, P., Labbani-Igbida, O., & Mouaddib, E. M. (2011). Robust free space segmentation using active contours and monocular omnidirectional vision. In 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 2877–2880, IEEE.

  • Renaudeau, B., Labbani-Igbida, O.,& Mourioux, G. (2018). Hybrid map mosaicing: A novel approach for large area mapping. In 2018 IEEE International Conference on Simulating, Modeling, and Programming for Autonomous Robots (SIMPAR)(to appear), IEEE.

  • Roman, K., Stumm, E., Taylor, Z., Mueggler, E., Delmerico, J., Scaramuzza, D.,  Siegwart, R., Hutter, M., & others, (2016). Collaborative localization of aerial and ground robots through elevation maps. In 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

  • Saska, M., Vonásek, V., Krajník, T., & Přeučil, L. (2014). Coordination and navigation of heterogeneous MAV-UGV formations localized by a ‘hawk-eye’-like approach under a model predictive control scheme. The International Journal of Robotics Research, 33, 1393–1412.

    Article  Google Scholar 

  • Singh, M., Chatterjee, A., & Chaudhury, S. (1997). Matching structural shape descriptions using genetic algorithms. Pattern Recognition, 30, 1451–1462.

    Article  Google Scholar 

  • Vidal, T., Berger, C., Sola, J.,& Lacroix, S. (2011). Environment modeling for cooperative aerial/ground robotic systems. In Robotics research, pp. 681–696, Springer.

  • Zaslavskiy, M., Bach, F., & Vert, J.-P. (2009). A path following algorithm for the graph matching problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 2227–2242.

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank 4DVirtualiz company that allows us to evaluate our algorithm with the help of their software. The latter was very useful to make simulations in realistic environments.

Author information

Authors and Affiliations

  1. Xlim Institute, UMR CNRS # 7252, University of Limoges, 16 rue Atlantis, 87068, Limoges, France

    Brice Renaudeau, Ouiddad Labbani-Igbida & Gilles Mourioux

Authors
  1. Brice Renaudeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ouiddad Labbani-Igbida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gilles Mourioux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brice Renaudeau.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is one of the several papers published in Autonomous Robots comprising the Special Issue on Multi-Robot and Multi-Agent Systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Renaudeau, B., Labbani-Igbida, O. & Mourioux, G. Air-ground cooperative topometric mapping of traversable ground. Auton Robot 44, 705–720 (2020). https://doi.org/10.1007/s10514-019-09872-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-019-09872-1

Keywords

Search

Navigation