Advertisement

D-SLAM: Decoupled Localization and Mapping for Autonomous Robots

  • Zhan Wang
  • Shoudong Huang
  • Gamini Dissanayake
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 28)

Abstract

The main contribution of this paper is the reformulation of the simultaneous localization and mapping (SLAM) problem for mobile robots such that the mapping and localization can be treated as two concurrent yet separated processes: D-SLAM (decoupled SLAM). It is shown that SLAM can be decoupled into solving a non-linear static estimation problem for mapping and a low-dimensional dynamic estimation problem for localization. The mapping problem can be solved using an Extended Information Filter where the information matrix is shown to be exactly sparse. A significant saving in the computational effort can be achieved for large scale problems by exploiting the special properties of sparse matrices. An important feature of D-SLAM is that the correlation among landmarks are still kept and it is demonstrated that the uncertainty of the map landmarks monotonically decrease. The algorithm is illustrated through computer simulations and experiments.

Keywords

Information Matrix Autonomous Robot Landmark Location Robot Location Good Initial Guess 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dissanayake G, Newman P, Clark S, Durrant-Whyte H, Csorba M (2001) “A solution to the simultaneous localization and map building (SLAM) problem,” IEEE Trans. on Robotics and Automation, vol. 17, pp. 229–241CrossRefGoogle Scholar
  2. 2.
    Guivant JE, Nebot EM (2001) “Optimization of the simultaneous localization and map building (SLAM) algorithm for real time implementation,” IEEE Trans. on Robotics and Automation, vol. 17, pp. 242–257CrossRefGoogle Scholar
  3. 3.
    Newman P (2000) On the Structure and Solution of the Simultaneous Localization and Map Building Problem, PhD thesis, Australian Centre of Field Robotics, University of Sydney, SydneyGoogle Scholar
  4. 4.
    Castellanos JA, Neira J, Tardos JD (2001) “Multisensor fusion for simultaneous localization and map building,” IEEE Trans. on Robotics and Automation, vol. 17, 908–914CrossRefGoogle Scholar
  5. 5.
    Bosse M, Newman P, Leonard JJ, and Teller S (2004) “SLAM in Large-scale Cyclic Environments using the Atlas Framework”, International Journal on Robotics Research, vol. 23(12), pp. 1113–1139CrossRefGoogle Scholar
  6. 6.
    Folkesson J, Christensen HI (2004) “Graphical SLAM-A Self-Correcting Map,” In Proceedings IEEE International Conference on Robotics and Automation (ICRA), LA, New Orleans, pp. 383–390Google Scholar
  7. 7.
    Thrun S, Liu Y, Koller D, Ng AY, Ghahramani Z, Durrant-Whyte H (2004) “Simultaneous Localization and Mapping with Sparse Extended Information Filters,” International J. of Robotics Research, vol. 23, pp. 693–716CrossRefGoogle Scholar
  8. 8.
    Frese U (2005) “A Proof for the Approximate Sparsity of SLAM Information Matrices,” In Proceedings IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, pp. 331–337Google Scholar
  9. 9.
    Eustice RM, Walter M, Leonard JJ (2005) “Sparse Extended Information Filters: Insights into Sparsification,” In Proceedings of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, Alberta, Canada, August, pp. 641–648Google Scholar
  10. 10.
    Eustice RM, Singh H, Leonard JJ (2005) “Exactly sparse delayed-state filters,” In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, pp. 2428–2435Google Scholar
  11. 11.
    Csorba M, Uhlmann JK, Durrant-Whyte H (1997) “A suboptimal algorithm for automatic map building,” In Proceedings of 1997 American Control Conference, USA, pp. 537–541Google Scholar
  12. 12.
    Martinelli A, Tomatics N, Siegwart R (2004) “Open challenges in SLAM: An optimal solution based on shift and rotation invariants,” In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), LA, New Orleans, pp. 1327–1332Google Scholar
  13. 13.
    Frese U, Larsson P, Duckett T (2005) “A Multigrid Algorithm for Simultaneous Localization and Mapping,” IEEE Transactions on Robotics, vol. 21(2), pp. 1–12CrossRefGoogle Scholar
  14. 14.
    Chen L, Arambel PO, Mehra RK (2002) “Estimation under unknown correlation: covariance intersection revisited,” IEEE Transactions on Automatic Control, 47(11), pp. 1879–1882CrossRefMathSciNetGoogle Scholar
  15. 15.
    Wang Z, Huang S, Dissanayake G (2005) “Decoupling Localization and Mapping in SLAM Using Compact Relative Maps,” In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Edmonton, Alberta, Canada, pp. 1041–1046Google Scholar
  16. 16.
    Wang Z, Huang S, Dissanayake G (2005) “Implementation Issues and Experimental Evaluation of D-SLAM,” In Proceedings of the 5th International Conference on Field and Service Robotics (FSR), Port Douglas, Australia. pp. 153–164Google Scholar
  17. 17.
    Nebot EM, UTE Experimental Data from Victoria Park, available online http://www.acfr.usyd.edu.au/homepages/academic/enebot/experimental_data_ute.htmGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Zhan Wang
    • 1
  • Shoudong Huang
    • 1
  • Gamini Dissanayake
    • 1
  1. 1.ARC Centre of Excellence for Autonomous Systems (CAS) Faculty of EngineeringUniversity of TechnologySydneyAustralia

Personalised recommendations