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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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.
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.
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.
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.
Durrant-Whyte, H.F. 1987. Consistent integration and propagation of disparate sensor observations. International Journal of Robotics Research, 6(3):3–24.
Durrant-Whyte, H.F. 1988a. Integration, Coordination and Control of Multisensor Robot Systems. Kluwer Academic Publishers: Boston, Mass.
Durrant-Whyte, H.F. 1988b. Uncertain geometry in robotics. IEEE Journal of Robotics and Automation, 4(1):23–31.
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.
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 http://www.informatik.uni-freiburg.de/∼gutmann.
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.
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.
Lu, F. 1995. Shape registration using optimization for mobile robot navigation. Ph.D thesis, University of Toronto, Department of Computer Science, available as ftp://ftp.cs.yorku.ca/pub/eem/FengLuPh.Dthesis.ps.gz.
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 ftp://ftp.cs.yorku.ca/pub/eem/matching.ps.gz.
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.
SICK Laser range scanner. http://www.sick.de.
Smith, R.C. and Cheeseman, P. 1986. On the representation and estimation of spatial uncertainty. International Journal of Robotics Research, 5(4):56–68.
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.
About this article
Cite this article
Lu, F., Milios, E. Globally Consistent Range Scan Alignment for Environment Mapping. Autonomous Robots 4, 333–349 (1997). https://doi.org/10.1023/A:1008854305733
- sensor-based mobile robotics
- laser range scanning
- range scan registration
- range scan alignment