Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Cooperative simultaneous localisation and mapping using independent Rao–Blackwellised filters

Cooperative simultaneous localisation and mapping using independent Rao–Blackwellised filters

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study focuses on an approach to the multi-robot simultaneous localisation and mapping (SLAM) problem. The authors consider that a team of robots start the map building process from known initial poses and move through the environment observing a set of landmarks. In particular, the authors consider that the robots are equipped with vision sensors and are capable to compute the relative distance to natural visual landmarks. In the proposed approach, each robot computes an own global map and integrates its own observations in it. In addition, each robot is also capable of integrating the measurements obtained by other robots in his own map, thus cooperating to compute a global map using the information provided by all the robots in the team. In this way, each robot in the team builds a different global map. The approach presented here differs from others in the sense that each robot maintains an independent SLAM filter, and still each robot can introduce the observations of other robots in his own map. The authors present a set of experimental results obtained in a simulated environment that validate the proposed approach.

References

    1. 1)
      • Ballesta, M., Reinoso, O., Gil, A., Payá, L., Juliá, M.: `Map fusion of visual landmark-based maps', Proc. 40th Int. Symp. on Robotics, 11–13 March 2009, Barcelona, Spain, p. 75–81.
    2. 2)
    3. 3)
    4. 4)
      • Triebel, R., Burgard, W.: `Improving simultaneous mapping and localization', Proc. National Conf. on Artificial Intelligence (AAAI), 2005, p. 545–551.
    5. 5)
      • Nieto, J., Guivant, J., Nebot, E., Thrun, S.: `Real time data association for FastSLAM', Proc. IEEE Int. Conf. on Robotics & Automation (ICRA), 2003, p. 412–418.
    6. 6)
    7. 7)
      • S. Thrun , W. Burgard , D. Fox . (2005) Probabilistic robotics.
    8. 8)
    9. 9)
      • Murillo, A.C., Guerrero, J.J., Sagüés, C.: `SURF features for efficient robot localization with omnidirectional images', Proc. IEEE Int. Conf. on Robotics & Automation (ICRA), 2007, San Diego, CA, USA, p. 3901–3907.
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • Valls-Miró, J., Zhou, W., Dissanayake, G.: `Towards vision based navigation in large indoor environments', Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2006, Beijing, China, p. 2096–2102.
    20. 20)
    21. 21)
      • M. Ballesta , A. Gil , O. Reinoso , L. Payá , L.M. Jimenez . Map fusion in an independent multi-robot approach. WSEAS Trans. Syst. , 9 , 959 - 968
    22. 22)
      • Bay, H., Tuytelaars, T., Van Gool, L.: `SURF: Speeded up robust features', Proc. 9th European Conf. on Computer Vision, 2006, p. 511–517.
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • Lowe, D.: `Object recognition from local scale-invariant features', Proc. Int. Conf. on Computer Vision (ICCV), 1999, Kerkyra, Greece, p. 1150–1157.
    27. 27)
      • Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: `FastSLAM: a factored solution to the simultaneous localization and mapping problem', 18thNational Conf. on Artificial Intelligence, 2002, Menlo Park, CA, USA, p. 593–598.
    28. 28)
    29. 29)
      • Zhou, X.S., Roumeliotis, S.I.: `Multi-robot SLAM with unknown initial correspondence: the robot rendezvous case', Proc. 2006 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2006, Beijing, China, p. 1785–1792.
    30. 30)
    31. 31)
      • Konolige, K., Fox, D., Limketkai, B., Ko, J., Stewart, B.: `Map merging for distributed robot navigation', Proc. 2003 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2003, p. 212–217.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2011.0108
Loading

Related content

content/journals/10.1049/iet-cvi.2011.0108
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address