Optimal landmark selection for triangulation of robot position

Robotics and Autonomous Systems

Volumn 23, Issue 4, 1998, Pages 277-292

  Madsen, Claus B ^a   Andersen, Claus S ^a  

^a AALBORG UNIVERSITY   (Denmark)

Author keywords

Landmarks; Mobile robotics; Navigation; Performance characterization; Robustness; Self positioning; Template matching; Triangulation

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; POSITION CONTROL; UNCERTAIN SYSTEMS;

LEAST NOISE SENSITIVE POSITION ESTIMATES;

MOBILE ROBOTS;

EID: 0032121010     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0921-8890(98)00014-1     Document Type: Article

References (12)

1. C.S. Andersen, J.G.M. Conçalves, Determining the pose of a mobile robot using triangulation: A vision based approach, Technical Report Tech. Note No. I.95.179, EU Joint Research Center, December 1995.
2. S. Atiya, G. Hager, Real-time vision-based robot localization, IEEE Transactions on Robotics and Automation 9 (6) (1993) 785-800.
3. M. Betke, L. Gurvits, Mobile robot localization using landmarks, IEEE Transactions on Robotics and Automation 13 (2) (1997) 251-263.
4. S. Carlsson, Relative positioning from model indexing, Image and Vision Computing 12 (3) (1994) 179-186.
5. H.I. Christensen, N.O. Kirkeby, S. Kristensen, L. Knudsen, E. Granum, Model-driven vision for in-door navigation, Robotics and Autonomous Systems 12 (1994) 199-207.
6. D. Coombs, K. Roberts, Centering behavior using peripheral vision, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, New York, 1993, pp. 440-445.
7. J.L. Crowley, Mathematical foundations of navigation an perception for an autonomous mobile robot, in: L. Dorst (Ed.), Reasoning with Uncertainty in Robotics, Springer, Berlin, 1996.
8. R. Greiner, R. Isukapalli, Learning to select useful landmarks, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26 (3) (1996) 437-449 (Special Issue on Learning in Autonomous Robots).
9. R.M. Haralick, Propagating covariance in computer vision, in: Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem, Israel, 1994, pp. 493-498.
10. C.B. Madsen, A comparative study of the robustness of two pose estimation techniques, Machine Vision and Applications 5/6 (9) (1997) 291-303 (Special Issue on Performance Characterization).
11. J. Santos-Victor, G. Sandini, F. Curotto, S. Garibaldi, Divergent stereo for robot navigation: Learning from bees, in: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, IEEE Press, New York, 1993, pp. 434-439.
12. H. Takeda, C. Facchinetti, J.-C. Latombe, Planning the motions of a mobile robot in a sensory uncertainty field, IEEE Transactions on Pattern Analysis and Machine Intelligence 16 (10) (1994) 1002-1017.