Autonomous Robots

, Volume 4, Issue 4, pp 333–349 | Cite as

Globally Consistent Range Scan Alignment for Environment Mapping

  • F. Lu
  • E. Milios


A robot exploring an unknown environment may need to build a worldmodel from sensor measurements. In order to integrate all the framesof sensor data, it is essential to align the data properly. Anincremental approach has been typically used in the past, in whicheach local frame of data is aligned to a cumulative global model, andthen merged to the model. Because different parts of the model areupdated independently while there are errors in the registration,such an approach may result in an inconsistent model.

In this paper, we study the problem of consistent registration ofmultiple frames of measurements (range scans), together with therelated issues of representation and manipulation of spatialuncertainties. Our approach is to maintain all the local frames ofdata as well as the relative spatial relationships between localframes. These spatial relationships are modeled as random variablesand are derived from matching pairwise scans or from odometry. Thenwe formulate a procedure based on the maximum likelihood criterion tooptimally combine all the spatial relations. Consistency is achievedby using all the spatial relations as constraints to solve for thedata frame poses simultaneously. Experiments with both simulated andreal data will be presented.

sensor-based mobile robotics laser range scanning mapping range scan registration range scan alignment 


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  1. Ayache, N. and Faugeras, O.D. 1989. Maintaining representations of the environment of a mobile robot. IEEE Transactions on Robotics and Automation, 5(6):804–819.Google Scholar
  2. Chatila, R. and Laumond, J.P. 1985. Position referencing and consistent world modeling for mobile robots. In IEEE International Conference on Robotics and Automation, pp. 138–145.Google Scholar
  3. Cox, I.J. 1991. Blanche: An experiment in guidance and navigation of an autonomous robot vehicle. IEEE Transactions on Robotics and Automation, 7(2):193–204.Google Scholar
  4. Crowley, J.L. 1989. World modeling and position estimation for a mobile robot using ultrasonic ranging. In IEEE International Conference on Robotics and Automation, pp. 674–680.Google Scholar
  5. Durrant-Whyte, H.F. 1987. Consistent integration and propagation of disparate sensor observations. International Journal of Robotics Research, 6(3):3–24.Google Scholar
  6. Durrant-Whyte, H.F. 1988a. Integration, Coordination and Control of Multisensor Robot Systems. Kluwer Academic Publishers: Boston, Mass.Google Scholar
  7. Durrant-Whyte, H.F. 1988b. Uncertain geometry in robotics. IEEE Journal of Robotics and Automation, 4(1):23–31.Google Scholar
  8. Gonzalez, J., Reina, A., and Ollero, A. 1994. Map building for a mobile robot equipped with a 2D laser rangefinder. In IEEE International Conference on Robotics and Automation, pp. 1904–1909.Google Scholar
  9. Gutmann, J.-S. and Schlegel, C. 1996. AMOS: Comparison of scan matching approaches for self-localization in indoor environments. In Eurobot '96, Kaiserslautern, Germany, also Available in∼gutmann.Google Scholar
  10. Kriegman, D.J., Triendl, E., and Binford, T.O. 1989. Stereo vision and navigation in buildings for mobile robots. IEEE Transactions on Robotics and Automation, 5(6):792–803.Google Scholar
  11. Leonard, J., Durrant-Whyte, H., and Cox, I.J. 1990. Dynamic map building for an autonomous mobile robot. In IEEE/RSJ International Conference on Intelligent Robots and Systems.Google Scholar
  12. Lu, F. 1995. Shape registration using optimization for mobile robot navigation. Ph.D thesis, University of Toronto, Department of Computer Science, available as Scholar
  13. Lu, F. and Milios, E. 1997. Robot pose estimation in unknown environments by matching 2D range scans. Journal of Intelligent and Robotic Systems (to appear in), available as Scholar
  14. Moutarlier, P. and Chatila, R. 1989. Stochastic multisensory data fusion for mobile robot location and environment modelling. In 5th International Symposium on Robotics Research, pp. 85–94.Google Scholar
  15. SICK Laser range scanner. Scholar
  16. Smith, R.C. and Cheeseman, P. 1986. On the representation and estimation of spatial uncertainty. International Journal of Robotics Research, 5(4):56–68.Google Scholar
  17. Tang, Y.C. and Lee, C.S.G. 1992. A geometric feature relation graph formulation for consistent sensor fusion. IEEE Transactions on System, Man, and Cybernetics, 22(1):115–129.Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • F. Lu
    • 1
  • E. Milios
    • 1
  1. 1.Department of Computer ScienceYork UniversityNorth YorkCanada

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