Globally Consistent Range Scan Alignment for Environment Mapping
Tóm tắt
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.
Tài liệu tham khảo
citation_journal_title=IEEE Transactions on Robotics and Automation; citation_title=Maintaining representations of the environment of a mobile robot; citation_author=N. Ayache, O.D. Faugeras; citation_volume=5; citation_issue=6; citation_publication_date=1989; citation_pages=804-819; citation_id=CR1
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.
citation_journal_title=IEEE Transactions on Robotics and Automation; citation_title=Blanche: An experiment in guidance and navigation of an autonomous robot vehicle; citation_author=I.J. Cox; citation_volume=7; citation_issue=2; citation_publication_date=1991; citation_pages=193-204; citation_id=CR3
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.
citation_journal_title=International Journal of Robotics Research; citation_title=Consistent integration and propagation of disparate sensor observations; citation_author=H.F. Durrant-Whyte; citation_volume=6; citation_issue=3; citation_publication_date=1987; citation_pages=3-24; citation_id=CR5
citation_title=Integration, Coordination and Control of Multisensor Robot Systems; citation_publication_date=1988; citation_id=CR6; citation_author=H.F. Durrant-Whyte; citation_publisher=Kluwer Academic Publishers
citation_journal_title=IEEE Journal of Robotics and Automation; citation_title=Uncertain geometry in robotics; citation_author=H.F. Durrant-Whyte; citation_volume=4; citation_issue=1; citation_publication_date=1988; citation_pages=23-31; citation_id=CR7
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.
citation_journal_title=IEEE Transactions on Robotics and Automation; citation_title=Stereo vision and navigation in buildings for mobile robots; citation_author=D. Kriegman, E. Triendl, T.O. Binford; citation_volume=5; citation_issue=6; citation_publication_date=1989; citation_pages=792-803; citation_id=CR10
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.
citation_journal_title=International Journal of Robotics Research; citation_title=On the representation and estimation of spatial uncertainty; citation_author=R. Smith, P. Cheeseman; citation_volume=5; citation_issue=4; citation_publication_date=1986; citation_pages=56-68; citation_id=CR16
citation_journal_title=IEEE Transactions on System, Man, and Cybernetics; citation_title=A geometric feature relation graph formulation for consistent sensor fusion; citation_author=Y.C. Tang, C.S.G. Lee; citation_volume=22; citation_issue=1; citation_publication_date=1992; citation_pages=115-129; citation_id=CR17