Map Merging

Chapter
First Online:
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter presents a solution for merging feature-based maps in a robotic network with limited communication . We consider a team of robots exploring an unknown environment. Along its operation, each robot observes the environment and builds and maintains its local stochastic map of the visited region. Simultaneously, the robots communicate and build a global map of the environment. The communication between the robots is limited and, at every time instant, each robot can only exchange data with its neighboring robots . This problem has been traditionally addressed using centralized schemes or broadcasting methods. Instead, in this chapter we study a fully distributed approach which is implementable in scenarios with limited communication. This solution does not rely on a particular communication topology and does not require any central node, making the system robust to individual failures. Each robot computes and tracks the global map based on local interactions with its neighbors . Under mild connectivity conditions on the communication graph, the algorithm asymptotically converges to the global map. In addition, we analyze the convergence speed according to the information increase in the local maps. The results are validated through simulations.

Keywords

Map merging Map fusion Limited communication Distributed systems Parallel computation 
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References

  1. 1.
    P. Alriksson, A. Rantzer, Distributed Kalman filtering using weighted averaging, in International Symposium on Mathematical Theory of Networks and Systems, Kyoto, Japan, July 2006Google Scholar
  2. 2.
    R. Aragues, J. Cortes, C. Sagues, Distributed consensus algorithms for merging feature-based maps with limited communication. Robot. Auton. Syst. 59(3–4), 163–180 (2011)CrossRefGoogle Scholar
  3. 3.
    R. Aragues, J. Cortes, C. Sagues, Distributed consensus on robot networks for dynamically merging feature-based maps. IEEE Trans. Robot. 4, 850–854 (2012)Google Scholar
  4. 4.
    R. Aragues, J. Cortes, C. Sagues, Distributed map merging with consensus on common information, in European Control Conference, Zurich, Switzerland, July 2013, pp. 736–741Google Scholar
  5. 5.
    R. Aragues, C. Sagues, Y. Mezouar, Feature-based map merging with dynamic consensus on information increments, in IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 2013, pp. 736–741Google Scholar
  6. 6.
    R. Aragues, G. Shi, D.V. Dimarogonas, C. Sagues, K.H. Johansson, Y. Mezouar, Distributed algebraic connectivity estimation for undirected graphs with upper and lower bounds. Automatica 50, 3253–3259 (2014)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    R. Aragues, C. Sagues, Y. Mezouar, Feature-based map merging with dynamic consensus on information increments. Auton. Robot. 38, 243–259 (2015)CrossRefMATHGoogle Scholar
  8. 8.
    Y. Bar-Shalom, X. R. Li, T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software (Wiley, New York, 2004)Google Scholar
  9. 9.
    F. Bullo, J. Cortes, S. Martinez, Distributed Control of Robotic Networks. Applied Mathematics Series Princeton University Press, Princeton, 2009), http://coordinationbook.info
  10. 10.
    G. Calafiore, Distributed randomized algorithms for probabilistic performance analysis. Syst. Control Lett. 58(3), 202–212 (2009)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    G. Calafiore, F. Abrate, Distributed linear estimation over sensor networks. Int. J. Control 82(5), 868–882 (2009)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    R. Carli, A. Chiuso, L. Schenato, S. Zampieri, Distributed Kalman filtering based on consensus strategies. IEEE J. Sel. Areas Commun. 26, 622–633 (2008)CrossRefGoogle Scholar
  13. 13.
    D. W. Casbeer, R. Beard, Distributed information filtering using consensus filters, in American Control Conference, St. Louis, USA, June 2009, pp. 1882–1887Google Scholar
  14. 14.
    H. Jacky Chang, C.S. George Lee, Y. Charlie Hu, Yung-Hsiang Lu, Multi-robot SLAM with topological/metric maps, in IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, USA, October 2007, pp. 1467–1472Google Scholar
  15. 15.
    A. Cunningham, V. Indelman, F. Dellaert. DDF–SAM 2.0: consistent distributed smoothing and mapping. In IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 2013, pp. 5220–5227Google Scholar
  16. 16.
    A. Cunningham, K.M. Wurm, W. Burgard, F. Dellaert, Fully distributed scalable smoothing and mapping with robust multi–robot data association, in IEEE International Conference on Robotics and Automation, St. Paul, USA, May 2012, pp. 1093–1100Google Scholar
  17. 17.
    G. Dissanayake, P. Newman, S. Clark, H.F. Durrant-Whyte, M. Csorba, A solution to the simultaneous localization and map building (SLAM) problem. IEEE Trans. Robot. Autom 17(3), 229–241 (2001)CrossRefGoogle Scholar
  18. 18.
    D. Fox, J. Ko, K. Konolige, B. Limketkai, D. Schulz, B. Stewart, Distributed multirobot exploration and mapping. IEEE Proc. 94(7), 1325–1339 (2006)CrossRefMATHGoogle Scholar
  19. 19.
    R.A. Freeman, P. Yang, K.M. Lynch, Stability and convergence properties of dynamic average consensus estimators, in IEEE Conference on Decision and Control, San Diego, CA, December 2006, pp. 398–403Google Scholar
  20. 20.
    S. Grime, H.F. Durrant-Whyte, Data fusion in decentralized sensor networks. Control Eng. Pract. 2(5), 849–863 (1994)CrossRefGoogle Scholar
  21. 21.
    R.A. Horn, C.R. Johnson, Matrix Analysis (Cambridge University Press, Cambridge, 1985)CrossRefMATHGoogle Scholar
  22. 22.
    A. Howard, Multi-robot simultaneous localization and mapping using particle filters. Int. J. Robot. Res. 25(12), 1243–1256 (2006)CrossRefGoogle Scholar
  23. 23.
    G.P. Huang, N. Trawny, A.I. Mourikis, S.I. Roumeliotis, On the consistency of multi-robot cooperative localization, in Robotics: Science and Systems, Seattle, WA, USA, June 2009, pp. 65–72Google Scholar
  24. 24.
    G.P. Huang, N. Trawny, A.I. Mourikis, S.I. Roumeliotis, Observability-based consistent EKF estimators for multi-robot cooperative localization. Auton. Robot. 30(1), 99–122 (2011)CrossRefGoogle Scholar
  25. 25.
    S. Huang, Z. Wang, G. Dissanayake, U. Frese, Iterated d-slam map joining: evaluating its performance in terms of consistency, accuracy and efficiency. Auton. Robot. 27(4), 409–429 (2009)CrossRefGoogle Scholar
  26. 26.
    S. Julier, J.K. Uhlmann, General decentralised data fusion with covariance intersection (CI), in Handbook of Multisensor Data Fusion, ed. by D.L. Hall, J. Llinas (CRC Press, Boca Raton, 2001)Google Scholar
  27. 27.
    K. Konolige, J. Gutmann, B. Limketkai, Distributed map-making, in Workshop on Reasoning with Uncertainty in Robotics, International Joint Conference on Artificial Intelligence, Acapulco, Mexico, 2003Google Scholar
  28. 28.
    A. Leshem, L. Tong, Estimating sensor population via probabilistic sequential polling, IEEE Signal Process. Lett., 12(5):395–398 (2005)Google Scholar
  29. 29.
    K.Y.K. Leung, T.D. Barfoot, H. Liu, Decentralized localization of sparsely-communicating robot networks: a centralized-equivalent approach. IEEE Trans. Robot. 26(1), 62–77 (2010)CrossRefGoogle Scholar
  30. 30.
    K.Y.K. Leung, T.D. Barfoot, H.H.T. Liu, Decentralized cooperative simultaneous localization and mapping for dynamic and sparse robot networks, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, October 2010, pp. 3554–3561Google Scholar
  31. 31.
    T. Li, J.F. Zhang, Consensus conditions on multi-agent systems with time-varying topologies and stochastic communication noises. IEEE Trans. Autom. Control 55(9), 2043–2057 (2010)MathSciNetCrossRefGoogle Scholar
  32. 32.
    K.M. Lynch, I.B. Schwartz, P. Yang, R.A. Freeman, Decentralized environmental modeling by mobile sensor networks. IEEE Trans. Robot. 24(3), 710–724 (2008)CrossRefGoogle Scholar
  33. 33.
    E.M. Nebot, M. Bozorg, H.F. Durrant-Whyte, Decentralized architecture for asynchronous sensors. Auton. Robot. 6(2), 147–164 (1999)CrossRefGoogle Scholar
  34. 34.
    C.V. Nguyen, S. Izadi, D. Lovell, Modeling kinect sensor noise for improved 3d reconstruction and tracking, in International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, Zurich, Switzerland, October 2012, pp. 524–530Google Scholar
  35. 35.
    R. Olfati-Saber, Distributed Kalman filter with embedded consensus filters, in IEEE Conference on Decision and Control Seville, Spain, 2005, pp. 8179–8184Google Scholar
  36. 36.
    R. Olfati-Saber, Distributed Kalman filtering for sensor networks, in IEEE Conference on Decision and Control, New Orleans, LA, December 2007, pp. 5492–5498Google Scholar
  37. 37.
    R. Olfati-Saber, J.S. Shamma, Consensus filters for sensor networks and distributed sensor fusion, IEEE Conference on Decision and Control Sevilla, Spain, 2005, pp. 6698–6703Google Scholar
  38. 38.
    L.M. Paz, J.D. Tardos, J. Neira, Divide and conquer: EKF SLAM in \(O(n)\). IEEE Trans. Robot. 24(5), 1107–1120 (2008)CrossRefMATHGoogle Scholar
  39. 39.
    M. Pfingsthorn, B. Slamet, A. Visser, A scalable hybrid multi-robot SLAM method for highly detailed maps, in Lecture Notes in Artificial Intelligence, ed. by U. Visser, F. Ribeiro, T. Ohashi, F. Dellaert, vol. 5001 (Springer, Berlin, 2008), pp. 457–464Google Scholar
  40. 40.
    W. Ren, R.W. Beard, E.M. Atkins, Information consensus in multivehicle cooperative control. IEEE Control Syst. Mag. 27(2), 71–82 (2007)CrossRefGoogle Scholar
  41. 41.
    D. Rodríguez-Losada, F. Matía, A. Jiménez, Local maps fusion for real time multirobot indoor simultaneous localization and mapping, in IEEE International Conference on Robotics and Automation, New Orleans, USA, April 2004, pp. 1308–1313Google Scholar
  42. 42.
    S.I. Roumeliotis, G.A. Bekey, Distributed multirobot localization. IEEE Trans. Robot. Autom. 18(5), 781–795 (2002)CrossRefGoogle Scholar
  43. 43.
    D.P. Spanos, R. Olfati-Saber, R.M. Murray, Distributed sensor fusion using dynamic consensus, in IFAC World Congress, Prague, 2005Google Scholar
  44. 44.
    Y.G. Sun, L. Wang, G. Xie, Average consensus in networks of dynamic agents with switching topologies and multiple time-varying delays. Syst. Control Lett. 57(2), 175–183 (2008)MathSciNetCrossRefMATHGoogle Scholar
  45. 45.
    S. Thrun, Y. Liu, D. Koller, A. Ng, H. Durrant-Whyte, Simultaneous localisation and mapping with sparse extended information filters. Int. J. Robot. Res. 23(7–8), 693–716 (2004)CrossRefGoogle Scholar
  46. 46.
    S. Utete, H.F. Durrant-Whyte, Routing for reliability in decentralised sensing networks. Am. Control Conf. 2, 2268–2272 (1994)Google Scholar
  47. 47.
    D. Varagnolo, G. Pillonetto, L. Schenato, Distributed statistical estimation of the number of nodes in sensor networks, in IEEE Conference on Decision and Control Atlanta, USA, 2010, pp. 1498–1503Google Scholar
  48. 48.
    R. Vincent, D. Fox, J. Ko, K. Konolige, B. Limketkai, B. Morisset, C. Ortiz, D. Schulz, B. Stewart, Distributed multirobot exploration, mapping, and task allocation. Ann. Math. Artif. Intell. 52(1), 229–255 (2008)MathSciNetCrossRefMATHGoogle Scholar
  49. 49.
    S.B. Williams, H.Durrant-Whyte, Towards multi-vehicle simultaneous localisation and mapping, in IEEE International Conference on Robotics and Automation, Washington, DC, USA, May 2002, pp. 2743–2748Google Scholar
  50. 50.
    L. Xiao, S. Boyd, Fast linear iterations for distributed averaging. Syst. Control Lett. 53, 65–78 (2004)MathSciNetCrossRefMATHGoogle Scholar
  51. 51.
    L. Xiao, S. Boyd, S. Lall, A space-time diffusion scheme for peer-to-peer least-square estimation, in Symposium on Information Processing of Sensor Networks (IPSN), Nashville, TN, April 2006, pp. 168–176Google Scholar
  52. 52.
    X.S. Zhou, S.I. Roumeliotis, Multi-robot SLAM with unknown initial correspondence: the robot rendezvous case, in IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, October 2006, pp. 1785–1792Google Scholar
  53. 53.
    M. Zhu, S. Martínez, Discrete-time dynamic average consensus. Automatica 46(2), 322–329 (2010)MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  • Rosario Aragues
    • 1
  • Carlos Sagues
    • 1
  • Youcef Mezouar
    • 2
  1. 1.Instituto de Investigación en Ingeniería de Aragón University of ZaragozaSaragossaSpain
  2. 2.Institut Pascal, CNRSClermont Université, IFMAClermont-FerrandFrance

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