An Experimental Study of Robust Distributed Multi-robot Data Association from Arbitrary Poses

  • Erik NelsonEmail author
  • Vadim Indelman
  • Nathan Michael
  • Frank Dellaert
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 109)


In this work, we experimentally investigate the problem of computing the relative transformation between multiple vehicles from corresponding inter-robot observations during autonomous operation in a common unknown environment. Building on our prior work, we consider an EM-based methodology which evaluates sensory observations gathered over vehicle trajectories to establish robust relative pose transformations between robots. We focus on experimentally evaluating the performance of the approach as well as its computational complexity and shared data requirements using multiple autonomous vehicles (aerial robots). We describe an observation subsampling technique which utilizes laser scan autocovariance to reduce the total number of observations shared between robots. Employing this technique reduces run time of the algorithm significantly, while only slightly diminishing the accuracies of computed inter-robot transformations. Finally, we provide discussion on data transfer and the feasibility of implementing the approach on a mesh network.


Mesh Network Data Association Rotation Error Relative Transformation Robot Trajectory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Fenwick, J.W., Newman, P.M., Leonard, J.J.: Cooperative concurrent mapping and localization. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1810–1817, Washington (2002)Google Scholar
  2. 2.
    Bailey, T., Bryson, M., Hua, M., Vial, J., McCalman, L., Durrant-Whyte, H.: Decentralised cooperative localisation for heterogeneous teams of mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2859–2865, Shanghai (2011)Google Scholar
  3. 3.
    Montijano, E., Aragues, R., Sagues, C.: Distributed data association in robotic networks with cameras and limited communications. IEEE Trans. Robot. 29, 1408–1423 (2013)CrossRefGoogle Scholar
  4. 4.
    Olson, E., Leonard, J., Teller, S.: Robust range-only beacon localization. In: Auton, I.E.E.E. (ed.) Underwater Vehicles, pp. 66–75. Maine, Sebasco (2004)Google Scholar
  5. 5.
    Howard, A., Parker, L.E., Sukhatme, G.S.: Experiments with a large heterogeneous mobile robot team: exploration, mapping, deployment and detection. Int. J. Robot. Res. 25, 431–447 (2006)CrossRefGoogle Scholar
  6. 6.
    Zhou, X.S., Roumeliotis, S.I.: Multi-robot SLAM with unknown initial correspondence: the robot rendezvous case. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1785–1792, Beijing (2006)Google Scholar
  7. 7.
    Charrow, B., Michael, N., Kumar, V.: Cooperative multi-robot estimation and control for radio source localization. In: Proceedings of the International Symposium on Experimental Robotics, Quebec (2012)Google Scholar
  8. 8.
    Thrun, S., Thayer, S., Whittaker, W., Baker, C., Burgard, W., Ferguson, D., Hahnel, D., Montemerlo, M., Morris, A., Omohundro, Z., Reverte, C., Whittaker, W.: Autonomous exploration and mapping of abandoned mines. IEEE Robot. Autom. Mag. 11, 79–91 (2004)CrossRefGoogle Scholar
  9. 9.
    Michael, N., Shen, S., Mohta, K., Mulgaonkar, Y., Kumar, V., Nagatani, K., Okada, Y., Kiribayashi, S., Otake, K., Yoshida, K., Ohno, K., Takeuchi, E., Tadokoro, S.: Collaborative mapping of an earthquake-damaged building via ground and aerial robots. J. Field Robot. 29, 832–841 (2012)CrossRefGoogle Scholar
  10. 10.
    Cunningham, A., Wurm, K.M., Burgard, W., Dellaert, F.: Fully distributed scalable smoothing and mapping with robust multi-robot data association. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1093–1100, Saint Paul (2012)Google Scholar
  11. 11.
    Montijano, E., Martinez, S., Sagues, C.: Distributed robust data fusion based on dynamic voting. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 5893–5898, Shanghai (2011)Google Scholar
  12. 12.
    Indelman, V., Keyes, D., Nelson, E., Michael, N., Dellaert, F.: Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization. In: Proceedings of the IEEE International Conference on Robotics and Automation (To Appear), Hong Kong (2014)Google Scholar
  13. 13.
    Lu, F., Milios, E.: Robot pose estimation in unknown environments by matching 2D range scans. J. Intell. Robot. Syst. 18, 249–275 (1997)CrossRefGoogle Scholar
  14. 14.
    Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 20–25, Shanghai (2011)Google Scholar
  15. 15.
    Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing ICP variants on real-world data sets. Auton. Robots 34, 133–148 (2013)CrossRefGoogle Scholar
  16. 16.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  17. 17.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34, 189–206 (2013)CrossRefGoogle Scholar
  18. 18.
    Censi, A.: An accurate closed-form estimate of ICP’s covariance. In: Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, pp. 3167–3172 (2007)Google Scholar
  19. 19.
    Dellaert, F.: Factor graphs and GTSAM: a hands-on introduction. Technical Report GT-RIM-CP&R-2012-002 (2012)Google Scholar
  20. 20.
    Nieto, J., Bailey, T., Nebot, E.: Recursive scan-matching slam. Robot. Auton. Syst. 55, 39–49 (2007)CrossRefGoogle Scholar
  21. 21.
    Jun, J., Sichitiu, M.L.: The nominal capacity of wireless mesh networks. IEEE Wirel. Commun. 10, 8–14 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Erik Nelson
    • 1
    Email author
  • Vadim Indelman
    • 2
  • Nathan Michael
    • 1
  • Frank Dellaert
    • 2
  1. 1.Carnegie Mellon UniversityPittsburghUSA
  2. 2.Georgia Institute of TechnologyAtlantaUSA

Personalised recommendations